System and method for multi-client deployment of augmented reality instrument tracking

ABSTRACT

Methods and related systems and devices are described for performing various AR medical applications, including a method of guiding augmented reality (AR) intervention. In one aspect, a primary client device: receives model sets, an intervention plan having an intervention field, and session information about a session related to the AR intervention from a server; receives first real-time input data from a first input device; generates metrics by evaluating an execution of the intervention plan by comparing the intervention plan to the first real-time input data; displays real-time graphics, based at least in part on the metrics, spatially over the intervention field; receives real-time status data, from the server, about a replicate client device that joins the session; sends the first real-time input data, the metrics and the evaluation computed from the intervention plan, through the server, to the replicate client device.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present utility patent application is a continuation of, and claimsthe priority benefit of, U.S. patent application Ser. No. 16/882,703filed May 25, 2020, and entitled “System and Method for Multi-ClientDeployment of Augmented Reality Instrument Tracking”. U.S. patentapplication Ser. No. 16/882,703 in turn claims the benefit of U.S.Provisional Patent Application No. 62/852,763, filed May 24, 2019, andentitled “System and Method for Multi-Client Deployment of AugmentedReality Instrument Tracking”. The entire contents of U.S. ProvisionalPatent Application No. 62/852,763 and U.S. patent application Ser. No.16/882,703 are hereby incorporated by reference.

FIELD

Various embodiments are described herein that generally relate to asystem and method for multi-client deployment of augmented realityinstrument tracking.

BACKGROUND

The following paragraphs are provided by way of background to thepresent disclosure. They are not, however, an admission that anythingdiscussed therein is prior art or part of the knowledge of personsskilled in the art.

Augmented reality (AR) is a technology where computer-generatedinformation (e.g., imaging, sound, text, haptic feedback) issuperimposed on a view of the real world, thus providing a composite“augmented” view of reality. The combination of AR with personalizedsurgery has the potential to improve planning, intervention, guidance,and education through quantitative and spatial feedback. As a spatialcommunication medium, it enables better application and dissemination ofknowledge and expertise. Until recently, AR was limited to researchfacilities with specialized and expensive equipment. The currentgeneration of AR devices have made advances in technological innovation,affordability, and portability. The AR experience relies on acombination of technologies to realize a personalized and contextualspatial experience. Recent progress in mobile computing with differentsensor arrays has enabled AR experiences across smartphones and tablets.Many large technology companies appear to be committed to ARcapabilities on their devices and platforms for years to come.

In head-mounted AR devices, there have been recent developments thatmirror the past progress of Virtual Reality (VR) devices. The VRenvironment is completely virtual and computer-generated, unlike the ARenvironment where the goal is to enhance a person's actual reality byadding or superimposing additional information to better interact withcertain tasks. VR devices are best known from gaming applications.Consumer VR first demonstrated immersive interactions by combining thecomputing power of modern desktop computers to render hi-fidelitystereoscopic renderings at a high refresh rate with a large field ofview and motion tracking.

AR headsets take advantage of low-cost and low-power sensor arrays foundin mobile devices with the technology advancements of VR. Current ARheadsets are optical see-through devices that use beam combiners toroute stereoscopic renderings of virtual objects to fuse with the realworld. They use a comprehensive suite of sensors, such asaccelerometers, gyroscopes, RGB+depth (RGB-D) cameras, and microphones,to digitize the real world in three dimensions (3D), with machinelearning enabled interactivity from the user through voice and gestures.The technologies for the current generation of AR headsets are stillmaturing, but current AR devices are at a fraction of the previous cost.Common examples of the current generation of headsets include: HoloLensby Microsoft, and Google-backed Magic Leap.

AR has become a ubiquitous technology across devices and platformspotentially available to both clinicians and patients. As a malleablespatial medium that combines the virtual and real with evolving context,AR enables medical professionals to communicate and manage complexityencountered on a daily basis in personalized treatments. In personalizedsurgery, augmented reality may be a technical medium that can maximizeclinical performance and improve patient outcomes.

AR also has the ability to combine different technologies such asmulti-modal imaging, computer vision, computer graphics, navigation,human-machine interactions, and machine learning. It is able to fuse allthe complex information from these technologies and present them in aspatially coherent and contextually relevant way. However, there areshortcomings in the current state of the art of AR as applied to thecontext of intervention and surgery.

SUMMARY OF VARIOUS EMBODIMENTS

Various embodiments of a system and method for multi-client deploymentof augmented reality instrument tracking are provided according to theteachings herein that may be used in surgical planning, intervention,guidance, and/or education.

In one broad aspect, in accordance with the teachings herein, there isprovided a computer-implemented method of guiding augmented reality (AR)intervention using a primary client device and a server, the primaryclient device having a first processor, the method comprising:receiving, at the primary client device, model sets, an interventionplan having an intervention field, and session information about asession related to the AR intervention from the server; receiving, atthe primary client device, first real-time input data from the firstinput device; generating, at the first processor, metrics by determiningan evaluation of an execution of the intervention plan by comparing theintervention plan to the first real-time input data; displaying, on theprimary client device, real-time graphics, based at least in part on themetrics, spatially over the intervention field; receiving, at theprimary client device, real-time status data, from the server, about areplicate client device connected to the server after the replicateclient device joins the session; sending, from the primary clientdevice, the first real-time input data, through the server, to thereplicate client device within the session; sending, from the primaryclient device, the metrics and the evaluation computed from theintervention plan, through the server, to the replicate client devicewithin the session; receiving, at the primary client device, secondreal-time input data from the server, the second real-time input dataoriginating from the replicate client device and relating to theintervention plan; and displaying, at the primary client device,real-time graphics based at least in part on the second real-time inputdata from the replicate client device.

In at least one embodiment, for remotely observing the guided ARintervention using the replicate client device having a second processorand a second input device, the method further comprises: receiving, atthe replicate client device, the model sets, the intervention plan, andthe session information about the session related to the AR interventionfrom the server; receiving, at the replicate client device, the firstreal-time input data, the metrics, and the evaluation broadcasted fromthe primary client device; and displaying, on the replicate clientdevice, real-time graphics based at least in part on the model sets, theintervention plan, the first real-time input data, the metrics, and theevaluation.

In at least one embodiment, for providing remote mentoring of the guidedAR intervention, the method further comprises: receiving, at thereplicate client device, the second real-time input data from the secondinput device; and sending, from the replicate client device, the secondreal-time input data, through the server, to one or more additionalreplicate client devices connected to the server and the primary clientdevice.

In at least one embodiment, for managing multi-user AR collaboration,the method further comprises: receiving, at the server, local userinputs from the replicate client device providing remote instructions;sending the local user inputs through the server to the primary clientdevice; displaying remote video input on the replicate client device incombination with the model sets and the intervention plan, the modelsets including an underlying surface model; executing, by the replicateclient device, a pixel selection evaluator based at least in part on thelocal user inputs and the remote video input, thereby generating a firstpixel selection output; executing, by the replicate client device, amodel selection evaluator based at least in part on the model sets andthe first pixel selection output to map a pixel location in a renderwindow to a 3D location of the underlying surface model, therebygenerating a first model selection output; rendering, on the replicateclient device, first selected faces of the underlying surface modelbased at least in part on the first model selection output; andrendering, on the replicate client device, first traced pixels based atleast in part on the first pixel selection output.

In at least one embodiment, for managing the multi-user AR collaborationat the primary client device performing the AR intervention, the methodfurther comprises: processing remote user inputs on the primary clientdevice; receiving local video input from the primary client device;executing, by the primary client device, a pixel selection evaluatorbased at least in part on the remote user inputs and the local videoinput, thereby generating a second pixel selection output; executing, bythe primary client device, a model selection evaluator based at least inpart on the model sets and the remote user inputs, thereby generating asecond model selection output; rendering audio instructions based atleast in part on the remote user inputs at the primary client device;rendering second selected faces based at least in part on the secondpixel selection output at the primary client device; and renderingsecond traced pixels based at least in part on the second modelselection output at the primary client device.

In at least one embodiment, to synchronize devices and tracks of themulti-user AR collaboration, the method further comprises: storing thefirst real-time input data in a first buffer in corresponding firstdevice tracks of the primary client device; generating first clock ticksat the primary client device; processing the first real-time input datain the first buffer through a first filter chain from the first clockticks; generating first data frames from the first filter chain;receiving, at the server, the first data frames from the primary clientdevice having a first set of corresponding time stamps determined fromthe first clock ticks; storing the second real-time input data in asecond buffer in corresponding second device tracks of the replicateclient device; generating second clock ticks at the replicate clientdevice; processing the second real-time input data in the second bufferthrough a second filter chain from the second clock ticks; generatingsecond data frames from the second filter chain; receiving, at theserver, the second data frames from the replicate client device having asecond set of corresponding time stamps determined from the second clockticks; generating, at the server, combined data frames based at least inpart on the first data frames and the second data frames along with thefirst set of corresponding time stamps and the second set ofcorresponding time stamps; and storing the combined data frames in adatabase.

In at least one embodiment, the method further comprises: retrieving, bythe server, the combined data frames from the database; generating, bythe server, output clock ticks; extracting, by the server, a primaryclient data frame and a primary client time stamp from the combined dataframes for the primary client device corresponding to a current outputclock tick; extracting, by the server, a replicate client data frame anda replicate client time stamp from the combined data frames for thereplicate client device corresponding to the current output clock tick;combining, by the server, extracted data frames of the primary clientdevice and the replicate client device between server time stampscorresponding to current and previous output clock ticks; andbroadcasting, by the server, the combined data frames along withcorresponding time stamps to the primary client device and the replicateclient device.

In at least one embodiment, for guiding geometric resection by ARvisualization, the method further comprises: obtaining, by the server, aplurality of resection planes from the intervention plan; obtaining, bya client device, a plurality of active cut planes from a trackedinstrument from one of the first real-time input data or the secondreal-time input data; determining, by the client device, the evaluationby comparing at least one of the plurality of active cut planes to atleast one of the plurality of resection planes; calculating, by theclient device, the metrics to determine at least one of angle offset andtip-to-plane distance; calculating, by the client device, the faces ofthe surface model that intersects with the plane of the trackedinstrument; and producing, by the client device, the AR visualization bygenerating the trajectory of the tracked instrument, outlining anintersection of one of the plurality of active cut planes and the modelset, and displaying a color-coded angle offset and a tip-to-planedistance to indicate precision, wherein the client device is the primaryclient device or the replicate client device.

In at least one embodiment, the for guiding needle placement by ARvisualization, the method further comprises: obtaining, by the server, aplurality of line trajectories from the intervention plan, each of theline trajectories comprising an entrance point and a target point;obtaining, by a client device, a plurality of active instrument lineplacements from a tracked instrument from one of the first real-timeinput data or the second real-time input data; determining, by theclient device, the evaluation by comparing at least one of the pluralityof active instrument line placements to at least one of the plurality ofline trajectories; calculating, by the client device, the metrics todetermine at least one of tip-to-trajectory distance, tip-to-targetdistance, and instrument-to-trajectory angle; calculating, by the clientdevice, the closest point between the tracked instrument tip and theplanned trajectory; and producing, by the client device, the ARvisualization by generating a trajectory of the tracked instrument,generating an intersection of a trajectory of the tracked instrumentwith the target point, generating a line between a tip of the trackedinstrument and a planned line trajectory, and displaying a color-codedtip-to-trajectory distance, a tip-to-target distance, and aninstrument-to-trajectory angle to indicate precision, wherein the clientdevice is the primary client device or the replicate client device.

In at least one embodiment, for displaying critical structure avoidanceby AR visualization, the method further comprises: obtaining, by theserver, a first image of an intervention target and a critical structureimage of the intervention target from the intervention plan; obtaining,by a client device, a plurality of tool placements from one of the firstreal-time input data or the second real-time input data from a trackedinstrument; determining, by the client device, the evaluation bycomparing at least one of the plurality of tool placements to a no-flyzone obtained from an overlay of the critical structure image on thefirst image; calculating, by the client device, the metrics to determinean incidence of the at least one of the plurality of tool placementswith the no-fly zone; and displaying the AR visualization on the clientdevice by showing in-field alerts indicating placement or trajectory ofthe tracked instrument intersecting with the no-fly zone, wherein theclient device is the primary client device or the replicate clientdevice.

In one broad aspect, in accordance with the teachings herein, there isprovided a system for performing guiding augmented reality (AR)intervention for planning, intervention, guidance, and/or education formedical applications, wherein the system comprises: a server including:a database having: a plurality of data models that each have a pluralityof model set records, a plurality of plans records, a plurality ofrecordings records, and a plurality of instruments records; a pluralityof user records; and a plurality of session records; and at least oneprocessor that is operatively coupled to the database and configured toexecute program instructions for implementing: an HTTP server forproviding endpoints for queries and delivery of content, userauthentication, and management of sessions; and a WebSocket server toenable multi-client broadcast of data across device specific listeningchannels by setting up WebSocket clients; and a primary client devicethat is communicatively coupled to the server to interact with the HTTPserver and the WebSocket server, the primary client device including afirst processor and a first input device, the primary client devicebeing configured to: receive model sets, an intervention plan having anintervention field, and session information about a session related tothe AR intervention from the server; receive first real-time input datafrom the first input device; generate metrics by determining anevaluation of an execution of the intervention plan by comparing theintervention plan to the first real-time input data; display real-timegraphics, based at least in part on the metrics, spatially over theintervention field; receive real-time status data, from the server,about a replicate client device connected to the server after thereplicate client device joins the session; send the first real-timeinput data, through the server, to the replicate client device withinthe session; send the metrics and the evaluation computed from theintervention plan, through the server, to the replicate client devicewithin the session; receive second real-time input data from the server,the second real-time input data originating from the replicate clientdevice and relating to the intervention plan; and display real-timegraphics based at least in part on the second real-time input data fromthe replicate client device.

In at least one embodiment, the system further comprises the replicateclient device, the replicate client device having a second processor anda second input device, wherein for remotely observing the guided ARintervention the replicate client device is configured to: receive themodel sets, the intervention plan, and the session information about thesession related to the AR intervention from the server; receive thefirst real-time input data, the metrics, and the evaluation broadcastedfrom the primary client device; and display real-time graphics based atleast in part on the model sets, the intervention plan, the firstreal-time input data, the metrics, and the evaluation.

In at least one embodiment, for providing remote mentoring of the guidedAR intervention: the replicate client device is configured to: receivethe second real-time input data from the second input device; and sendthe second real-time input data, through the server, to one or moreadditional replicate client devices connected to the server and theprimary client device.

In at least one embodiment, for managing multi-user AR collaboration:the server is configured to receive local user inputs from the replicateclient device providing remote instructions and send the local userinputs to the primary client device; and the replicate client device isconfigured to: display remote video input in combination with the modelsets and the intervention plan, the model sets including an underlyingsurface model; execute a pixel selection evaluator based at least inpart on the local user inputs and the remote video input, therebygenerating a first pixel selection output; execute a model selectionevaluator based at least in part on the model sets and the first pixelselection output to map a pixel location in a render window to a 3Dlocation of the underlying surface model, thereby generating a firstmodel selection output; render first selected faces of the underlyingsurface model based at least in part on the first model selectionoutput; and render first traced pixels based at least in part on thefirst pixel selection output.

In at least one embodiment, for managing the multi-user AR collaborationat the primary client device performing the AR intervention, the primaryclient device is configured to: process remote user inputs; receivelocal video input; execute a pixel selection evaluator based at least inpart on the remote user inputs and the local video input, therebygenerating a second pixel selection output; execute a model selectionevaluator based at least in part on the model sets and the remote userinputs, thereby generating a second model selection output; render audioinstructions based at least in part on the remote user inputs; rendersecond selected faces based at least in part on the second pixelselection output; and render second traced pixels based at least in parton the second model selection output.

In at least one embodiment, to synchronize devices and tracks of themulti-user AR collaboration: the primary client device is configured to:store the first real-time input data in a first buffer in correspondingfirst device tracks of the primary client device; generate first clockticks; process the first real-time input data in the first bufferthrough a first filter chain from the first clock ticks; and generatefirst data frames from the first filter chain; the replicate clientdevice is configured to: store the second real-time input data in asecond buffer in corresponding second device tracks of the replicateclient device; generate second clock ticks at the replicate clientdevice; process the second real-time input data in the second bufferthrough a second filter chain from the second clock ticks; and generatesecond data frames from the second filter chain; and the server isconfigured to: receive, from the primary client device, the first dataframes having a first set of corresponding time stamps determined fromthe first clock ticks; receive the second data frames from the replicateclient device having a second set of corresponding time stampsdetermined from the second clock ticks; and generate combined dataframes based at least in part on the first data frames and the seconddata frames along with the first set of corresponding time stamps andthe second set of corresponding time stamps; and store the combined dataframes in a database.

In at least one embodiment, the server is further configured to:retrieve the combined data frames from the database; generate outputclock ticks; extract a primary client data frame and a primary clienttime stamp from the combined data frames for the primary client devicecorresponding to a current output clock tick; extract a replicate clientdata frame and a replicate client time stamp from the combined dataframes for the replicate client device corresponding to the currentoutput clock tick; combine extracted data frames of the primary clientdevice and the replicate client device between server time stampscorresponding to current and previous output clock ticks; and broadcastthe combined data frames along with corresponding time stamps to theprimary client device and the replicate client device.

In at least one embodiment, for guiding geometric resection by ARvisualization: the server is configured to obtain a plurality ofresection planes from the intervention plan and send the plurality ofresection planes to a client device; and the client device is configuredto: obtain a plurality of active cut planes from a tracked instrumentfrom one of the first real-time input data or the second real-time inputdata; determine the evaluation by comparing at least one of theplurality of active cut planes to at least one of the plurality ofresection planes; calculate the metrics to determine at least one ofangle offset and tip-to-plane distance; calculate the faces of thesurface model that intersects with the plane of the tracked instrument;and produce the AR visualization by generating the trajectory of thetracked instrument, outlining an intersection of one of the plurality ofactive cut planes and the model set, and displaying a color-coded angleoffset and a tip-to-plane distance to indicate precision, wherein theclient device is the primary client device or the replicate clientdevice.

In at least one embodiment, for guiding needle placement by ARvisualization: the server is configured to obtain and send a pluralityof line trajectories from the intervention plan to a client device,where each of the line trajectories comprise an entrance point and atarget point; and the client device is configured to: obtain a pluralityof active instrument line placements from a tracked instrument from oneof the first real-time input data or the second real-time input data;determine the evaluation by comparing at least one of the plurality ofactive instrument line placements to at least one of the plurality ofline trajectories; calculate the metrics to determine at least one oftip-to-trajectory distance, tip-to-target distance, andinstrument-to-trajectory angle; calculate the closest point between thetracked instrument tip and the planned trajectory; and produce the ARvisualization by generating a trajectory of the tracked instrument,generating an intersection of a trajectory of the tracked instrumentwith the target point, generating a line between a tip of the trackedinstrument and a planned line trajectory, and displaying a color-codedtip-to-trajectory distance, a tip-to-target distance, and aninstrument-to-trajectory angle to indicate precision, wherein the clientdevice is the primary client device or the replicate client device.

In at least one embodiment, for displaying critical structure avoidanceby AR visualization: the server is configured to obtain and send a firstimage of an intervention target and a critical structure image of theintervention target from the intervention plan to a client device; andthe client device is configured to: obtain a plurality of toolplacements from one of the first real-time input data or the secondreal-time input data from a tracked instrument; determine the evaluationby comparing at least one of the plurality of tool placements to ano-fly zone obtained from an overlay of the critical structure image onthe first image; calculate the metrics to determine an incidence of theat least one of the plurality of tool placements with the no-fly zone;and display the AR visualization on the client device by showingin-field alerts indicating placement or trajectory of the trackedinstrument intersecting with the no-fly zone, wherein the client deviceis the primary client device or the replicate client device.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method of managing a multi-useraugmented reality (AR) collaboration, the method comprising: receivingfirst model sets from a first client device; receiving local user inputsfrom the first client device; processing remote video input onto thefirst client device; executing a model selection evaluator based on thefirst model sets and the local user inputs; executing a pixel selectionevaluator based on the local user inputs and the remote video input;rendering selected faces based on the output from the model selectionevaluator; rendering traced pixels based on the output from the pixelselection evaluator; receiving second model sets from a second clientdevice; processing remote user inputs onto the second client device;receiving local video input from the second client device; executing amodel selection evaluator based on the second model sets and the remoteuser inputs; executing a pixel selection evaluator based on the remoteuser inputs and the local video input; rendering audio instructionsbased on the remote user inputs; rendering selected faces based on theoutput from the pixel selection based on the pixel selection evaluator;rendering traced pixels based on the output from the model selectionevaluator; and managing socket broadcasts of at least one of the localuser inputs, the remote video input, the remote user inputs, and thelocal video input.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method of inside-out tracking using aclient device having a processor, the method comprising: receiving toolimage data of a tool at the processor from a first camera; determiningtool coordinates from the tool image data using the processor; mappingthe tool coordinates to device coordinates using the processor; mappingthe device coordinates to client device coordinates using the processor;mapping the client device coordinates to reference coordinates using theprocessor; generating a virtual-space image of the tool by applying aregistration transform to the reference coordinates using the processor;and displaying the virtual-space image of the tool on a display.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method of controlling devices andtracks, the method comprising: generating clock ticks; receiving a firstplurality of input data from a first device into a buffer; having afirst set of corresponding time stamps determined from the clock ticks;processing the buffer data on the clock ticks to generate the firstplurality of data frames along with the first set of corresponding timestamps; receiving a second plurality of input data from a second deviceinto a buffer; having a second set of corresponding time stampsdetermined from the clock ticks; processing the buffer data on the clockticks to generate the second plurality of data frames along with thesecond set of corresponding time stamps; sending each of the pluralityof data frames and time stamps to the server; and outputting each of theplurality of data frames to an AR application.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method for performing AR-assistedscientific output augmentation at a client device having a processor,the method comprising: receiving a surface representation of a Cone BeamComputed Tomography (CBCT) model and a corresponding figure from ajournal article at the client device; anchoring the CBCT model to thefigure image using the processor; calculating a pose using the processorby matching known spatial points of the figure image to image points ofthe CBCT model via homography; and displaying the pose on a display.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method for performing AR-assistedsurgical procedure walkthrough at a client device having a processor,the method comprising: receiving a virtual surgical plan at the clientdevice; receiving a virtual model at the client device; embedding thevirtual model to a physical object using the processor; receiving toolmanipulation data from user input at the client device; modifying a viewof the virtual model in relation to the physical object using theprocessor based on the tool manipulation data; determining metrics byusing the processor to apply spatial registration and track the toolused in execution of the virtual surgical plan; and providing feedbackat the client device based on the metrics.

In another broad aspect, in accordance with the teachings herein, thereis provided a computer-implemented method for performing outside-intracking at a client device having a processor, the method comprising:receiving device image data at the processor from a first camera;determining device coordinates from the device image data using theprocessor; mapping the device coordinates to device sensor coordinatesusing the processor; mapping the device sensor coordinates todevice-tracker coordinates using the processor; mapping thedevice-tracker coordinates to device-reference coordinates using theprocessor; applying a first registration transform to thedevice-reference coordinates using the processor to display the devicein virtual space; receiving tool image data at the processor from asecond camera; determining tool coordinates from the tool image datausing the processor; mapping the tool coordinates to tool sensorcoordinates using the processor; mapping the tool sensor coordinates totool-tracker coordinates using the processor; mapping the tool-trackercoordinates to tool-reference coordinates using the processor;generating a virtual-space image of the tool by applying a secondregistration transform to the tool-reference coordinates using theprocessor; and displaying the virtual-space image of the tool on adisplay.

In another broad aspect, in accordance with the teachings herein, thereis provided a device for performing an AR method related to at least oneof planning, intervention, guidance, and education for medicalapplications, wherein the device comprises: a display for displaying ARimages; a user interface for receiving user input at the device; amemory for storing program instructions for performing the AR method;and a processor that is operatively coupled to the display, the userinterface and the memory, wherein the processor is configured to executethe program instructions for performing a method according to any one ofthe methods described in accordance with the teachings herein.

In another broad aspect, in accordance with the teachings herein, thereis provided a system for performing allowing at least one client devicefor perform an AR method related to at least one of planning,intervention, guidance, and education for medical applications, whereinthe system comprises: a server including: a database having: a pluralityof data models that each have a plurality of model set records, aplurality of plans records, a plurality of recordings records and aplurality of instruments records; a plurality of user records; and aplurality of session records; and at least one processor that isoperatively coupled to the database and configured to execute programinstructions for implementing: an HTTP server for providing endpointsfor queries and delivery of content, user authentication, and managementof sessions; and a WebSocket server to enable multi-client broadcast ofdata across device specific listening channels by setting up WebSocketclients; and at least one client device that is communicatively coupledto the server to interact with the HTTP server and the WebSocket server,the at least one client device being defined according to the any of theteachings herein and being configured to perform any one of the methodsdescribed in accordance with the teachings herein.

Other features and advantages of the present application will becomeapparent from the following detailed description taken together with theaccompanying drawings. It should be understood, however, that thedetailed description and the specific examples, while indicatingpreferred embodiments of the application, are given by way ofillustration only, since various changes and modifications within thespirit and scope of the application will become apparent to thoseskilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the various embodiments described herein,and to show more clearly how these various embodiments may be carriedinto effect, reference will be made, by way of example, to theaccompanying drawings which show at least one example embodiment, andwhich are now described. The drawings are not intended to limit thescope of the teachings described herein.

FIG. 1A illustrates an example embodiment of an augmented reality (AR)system for multi-client broadcasting and streaming in accordance withthe teachings herein.

FIG. 1B shows an example embodiment of a server that can be used withthe AR system of FIG. 1A.

FIG. 2 shows an example embodiment of a multi-client configuration of aWebSocket server connected to client devices in the AR system of FIG.1A.

FIG. 3 shows an example of a scene graph for an outside-in navigationsetup.

FIG. 4 shows an example of an outside-in tracking setup used inosteotomy.

FIG. 5 shows an example of a scene graph for inside-out tracking.

FIG. 6 shows an example of an inside-out tracking setup used inosteotomy.

FIG. 7 shows an example of pose calculation of two camera devicesviewing a common reference object with known coordinates and spatialpoints.

FIG. 8 shows an example of a scene graph equivalency for two devicesviewing a common reference object.

FIG. 9 shows an example of pose calculation of two camera devicesviewing a common planar object with known coordinates and spatialpoints.

FIG. 10 shows an example of a controller connected to a device and acorresponding buffer.

FIG. 11 shows an example of metrics used in needle guidance.

FIG. 12 shows an example of an intersection of a needle with anultrasound plane enabling out-of-plane advancement of the needle.

FIG. 13 shows an example of yaw, pitch, and roll pivots for an osteotomeand planar tools.

FIG. 14 shows a flow chart of an example embodiment of a method ofmanaging critical structure avoidance in the AR system of FIG. 1A.

FIG. 15 shows a flow chart of an example embodiment of a method ofmanaging geometric resection in the AR system of FIG. 1A.

FIG. 16 shows a flow chart of an example embodiment of a method ofguiding a needle in the AR system of FIG. 1A.

FIG. 17 shows a flow chart of an example embodiment of a method ofmanaging a procedure walkthrough in the AR system of FIG. 1A.

FIG. 18 shows a flow chart of an example embodiment of a method oftracking a figure and enhancing a publication in the AR system of FIG.1A.

FIG. 19 shows a flow chart of an example embodiment of a method ofmanaging an assessment and review in the AR system of FIG. 1A.

FIG. 20 shows a flow chart of an example embodiment of a method ofmanaging remote collaboration in the AR system of FIG. 1A.

FIG. 21 shows a flow chart of an example embodiment of a method ofapplication management in the AR system of FIG. 1A.

FIG. 22 shows a flow chart of an example embodiment of a method of loginmanagement in the AR system of FIG. 1A.

FIG. 23 shows a flow chart of an example embodiment of a method ofsession creation in the AR system of FIG. 1A.

FIG. 24 shows a flow chart of an example embodiment of a method ofsession joining in the AR system of FIG. 1A.

FIG. 25 shows a flow chart of an example embodiment of a method of dataloading in the AR system of FIG. 1A.

FIG. 26 shows a flow chart of an example embodiment of a method ofsetting up a scene in the AR system of FIG. 1A.

FIG. 27 shows a flow chart of an example embodiment of a method ofsetting up devices in the AR system of FIG. 1A.

FIG. 28 shows a flow chart of an example embodiment of a method ofapplication cleanup in the AR system of FIG. 1A.

FIG. 29 shows a flow chart of an example embodiment of a method ofleaving a session in the AR system of FIG. 1A.

FIG. 30 shows a flow chart of an example embodiment of a method ofquerying metadata in the AR system of FIG. 1A.

FIG. 31 shows an example of playback of a navigated osteotomy on afemur.

FIG. 32 shows an example of an ablative needle procedure.

FIG. 33 shows an example visualization of a critical structure andno-fly zones in a skull model.

FIG. 34 shows an example virtual skull model mapped to a physicalobject.

FIG. 35 shows an example of an AR-enhanced paper.

FIG. 36 shows an example layout of a chat window.

FIG. 37 shows an example of a sphere mesh appearing on a model set atthe beginning of a procedure for creating a text annotation.

FIG. 38 shows an example of an input window to enter additionalinformation for the text annotation FIG. 37 .

FIG. 39 shows an example of the additional information viewable in thetext annotation of FIG. 37 .

FIG. 40 shows an example of control of a client device viewpoint.

FIG. 41 shows a flow chart of an example embodiment of a method of textto speech conversion in the AR system of FIG. 1A.

FIG. 42 shows a flow chart of an example embodiment of a method ofspeech to text conversion in the AR system of FIG. 1A.

FIG. 43 shows an example of visual feedback of misalignment during ARguidance of osteotomy.

FIG. 44 shows an example of visual feedback of proper alignment duringAR guidance of osteotomy.

FIG. 45 shows an example of visual feedback during AR guidance of needleinsertion.

FIG. 46 shows a flow chart of an example embodiment of a method ofguiding AR intervention in the AR system of FIG. 1A.

FIG. 47 shows a flow chart of an example embodiment of a method forremotely observing a guided AR intervention in the AR system of FIG. 1A.

Further aspects and features of the example embodiments described hereinwill appear from the following description taken together with theaccompanying drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various embodiments in accordance with the teachings herein will bedescribed below to provide an example of at least one embodiment of theclaimed subject matter. No embodiment described herein limits anyclaimed subject matter. The claimed subject matter is not limited todevices, systems, or methods having all of the features of any one ofthe devices, systems, or methods described below or to features commonto multiple or all of the devices, systems, or methods described herein.It is possible that there may be a device, system, or method describedherein that is not an embodiment of any claimed subject matter. Anysubject matter that is described herein that is not claimed in thisdocument may be the subject matter of another protective instrument, forexample, a continuing patent application, and the applicants, inventorsor owners do not intend to abandon, disclaim, or dedicate to the publicany such subject matter by its disclosure in this document.

It will be appreciated that for simplicity and clarity of illustration,where considered appropriate, reference numerals may be repeated amongthe figures to indicate corresponding or analogous elements. Inaddition, numerous specific details are set forth in order to provide athorough understanding of the embodiments described herein. However, itwill be understood by those of ordinary skill in the art that theembodiments described herein may be practiced without these specificdetails. In other instances, well-known methods, procedures, andcomponents have not been described in detail so as not to obscure theembodiments described herein. Also, the description is not to beconsidered as limiting the scope of the embodiments described herein.

It should also be noted that the terms “coupled” or “coupling” as usedherein can have several different meanings depending on the context inwhich these terms are used. For example, the terms coupled or couplingcan have a mechanical or electrical connotation. For example, as usedherein, the terms coupled or coupling can indicate that two elements ordevices can be directly connected to one another or connected to oneanother through one or more intermediate elements or devices via anelectrical signal, electrical connection, or a mechanical elementdepending on the particular context.

It should also be noted that, as used herein, the wording “and/or” isintended to represent an inclusive-or. That is, “X and/or Y” is intendedto mean X or Y or both, for example. As a further example, “X, Y, and/orZ” is intended to mean X or Y or Z or any combination thereof.

It should be noted that terms of degree such as “substantially”,“about”, and “approximately” as used herein mean a reasonable amount ofdeviation of the modified term such that the end result is notsignificantly changed. These terms of degree may also be construed asincluding a deviation of the modified term, such as by 1%, 2%, 5%, or10%, for example, if this deviation does not negate the meaning of theterm it modifies.

Furthermore, the recitation of numerical ranges by endpoints hereinincludes all numbers and fractions subsumed within that range (e.g., 1to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to beunderstood that all numbers and fractions thereof are presumed to bemodified by the term “about” which means a variation of up to a certainamount of the number to which reference is being made if the end resultis not significantly changed, such as 1%, 2%, 5%, or 10%, for example.

The example embodiments of the devices, systems, or methods described inaccordance with the teachings herein may be implemented as a combinationof hardware and software. For example, at least some embodiments or aportion of the embodiments described herein may be implemented, at leastin part, by using one or more computer programs, executing on one ormore programmable devices comprising at least one processing element andat least one storage element (i.e., at least one volatile memory elementand at least one non-volatile memory element). The hardware may compriseinput devices including at least one of a touch screen, a keyboard, amouse, buttons, keys, sliders, and the like, as well as one or more of adisplay, a printer, and the like depending on the implementation of thehardware.

It should also be noted that there may be some elements that are used toimplement at least part of the embodiments described herein that may beimplemented via software that is written in a high-level procedurallanguage such as object-oriented programming. The program code may bewritten in C⁺⁺, C#, Python, JavaScript, or any other suitableprogramming language and may comprise modules or classes, as is known tothose skilled in object-oriented programming. Alternatively, or inaddition thereto, some of these elements implemented via software may bewritten in assembly language, machine language, or firmware as needed.In either case, the language may be a compiled or interpreted language.

At least some of these software programs may be stored on acomputer-readable medium such as, but not limited to, ROM, a magneticdisk, an optical disc, a USB key, and the like that is readable by adevice having at least one processor, an operating system, and theassociated hardware and software that is necessary to implement thefunctionality of at least one of the embodiments described herein. Thesoftware program code, when read by the device, configures the device tooperate in a new, specific, and predefined manner (e.g., as aspecific-purpose computer) in order to perform at least one of themethods described herein.

At least some of the programs associated with the devices, systems, andmethods of the embodiments described herein may be capable of beingdistributed in a computer program product comprising a computer-readablemedium that bears computer-usable instructions, such as program code,for one or more processing units. The medium may be provided in variousforms, including non-transitory forms such as, but not limited to, oneor more diskettes, compact disks, tapes, chips, and magnetic andelectronic storage. In alternative embodiments, the medium may betransitory in nature such as, but not limited to, wire-linetransmissions, satellite transmissions, internet transmissions (e.g.,downloads), media, digital and analog signals, and the like. Thecomputer-usable instructions may also be in various formats, includingcompiled and non-compiled code.

In accordance with the teachings herein, there are provided variousexample embodiments for systems and methods for multi-client deploymentof augmented reality (AR) instrument tracking which may be used in thecontext of at least one of surgical planning, intervention, guidance,and education. The example embodiments are not necessarily limited tomulti-client deployment of AR, but may also be applicable to mixedreality (MR), virtual reality (VR), augmented virtuality (AV), andsimilar modes of technology.

For example, in at least one embodiment, an AR system is provided thatutilizes multi-client broadcasting and streaming to allow real-timeguided AR intervention (e.g., surgery) using metrics. The system (or aclient device that is part of the AR system) receives or loads anintervention (e.g., surgical) plan and session information related tothe AR intervention (e.g., surgery). The session information may includeaccess credentials, a session identifier, and a port, which can beobtained from a server in the AR system. The server can facilitatecommunication between multiple client devices. The system (or clientdevice) receives real-time data (e.g., position and orientation) of atool (e.g., an osteotome) from an input device, such as an AR headset(also known as “AR glasses”) or a mobile device (e.g., a tablet device).The server can permit at least one other client device to join thesession. The system (or client device) can receive real-time data, fromthe server, of the at least one other client device joined in thesession. The system (or client device) can send real-time data to theserver to be broadcast to any (or all) of the client devices joined inthe session. The system (or client device) modifies the real-time databy applying volume operations (e.g., dilation and erosion) or meshoperations (e.g., space portioning) based on the intervention planand/or the session information. The system (or client device) modifiesthe real-time data further by determining intersections. The system (orclient device) generates metrics by determining an evaluation (i.e.,scoring) of an execution of the intervention (e.g., surgical) plan bycomparing the initial intervention (e.g., surgical) plan to userexecution. The system (or client device) can display real-time graphics(e.g., shaders) based on the evaluation.

In an implementation of the at least one embodiment of the AR systemdescribed above, the real-time guided AR intervention (e.g., surgery)uses metrics to guide geometric resection. In particular, theintervention plan comprises a plurality of resection planes; thereal-time data comprises a plurality of active cut planes; theevaluation is determined by comparing at least one of the plurality ofactive cut planes to at least one of the plurality of resection planes;and the metrics comprise at least one of angle offset and tip-to-planedistance.

In another implementation of the at least one embodiment of the ARsystem described above, the real-time guided AR intervention (e.g.,surgery) uses metrics to guide needle placement. In particular, theintervention plan comprises a plurality of line trajectories, each ofthe line trajectories comprising an entrance point and a target point;the real-time data comprises a plurality of active needle placements;the evaluation is determined by comparing at least one of the pluralityof active needle placements to at least one of the plurality of linetrajectories; and the metrics comprise at least one of tip-to-trajectorydistance, tip-to-target distance, and needle-to-trajectory angle.

In yet another implementation of the at least one embodiment of the ARsystem described above, the real-time guided AR intervention (e.g.,surgery) uses metrics to guide critical structure avoidance. Inparticular, the intervention plan comprises a first image of anintervention target and a critical structure image of the interventiontarget; the real-time data comprises a plurality of tool placements; theevaluation is determined by comparing at least one of the plurality oftool placements to a no-fly zone obtained from an overlay of thecritical structure image on the first image; and the metrics comprisethe incidence of the at least one of the plurality of tool placementswith the no-fly zone.

In at least one embodiment, the system provides a 3D comparison of auser's actual movements to a stored “surgical plan”. If a user makes theexact same movements, for example, as the pre-defined surgical plan, thesystem may provide the user with a very high score. The shaders aredisplayed as computer graphics that describe how a surface behaves,which may reflect how the system renders data or content in real time.For example, the shader can be used to show performance metrics,graphically represented by a different color or overlays. The shaderscan be, for example, opaque, semi-transparent, or outlines. The shadersmay also be used so that the meshes can be seen in ways that enhance theAR experience. For example, the shaders can be used to indicate how wella user performed compared to the predefined surgical plan. It will beappreciated that any reference to a “surgical plan” in this disclosureapplies equally to an “intervention plan”, and vice versa.

In another aspect, a technical problem is providing a similar experienceto different users across different devices and different platforms indifferent locations (e.g., mobile, browser, headset), where there mayalso be vendor differences (e.g., Apple, Android, Windows). Currentmobile devices have more computing power than headsets, yet mobiledevices require rendering for a single display as opposed to headsets,which uses dual renderings (as needed for each eye). At least one of theembodiments described in accordance with the teachings herein provides atechnical solution to this problem by creating models at differentlevels of detail and optimizing settings to accommodate for thedifferent levels of computing power of different devices that areoperating on the same source data (e.g., refresh rate, clipping planes,and polygon count).

In another aspect, another technical problem is providingdevice-specific implementations in a development environment thatconsists of cross-platform engines and libraries (e.g., Unity andVuforia). For example, outline shaders (that apply a simple outlinearound an object) do not display correctly on headsets, but they do onmobile devices. At least one of the embodiments described in accordancewith the teachings herein provides a technical solution to this problemby having different shader implementations on headsets to displaycutting planes in a surgical plan.

In another aspect, another technical problem is that the same-voicerecognition implementation works on Android and Microsoft devices, butnot with iOS devices, or that same-voice recognition is in varyingdegrees of implementation with Microsoft devices being the mostdeveloped. At least one of the embodiments described in accordance withthe teachings herein provides a technical solution by having a separateiOS implementation.

In another aspect, another technical problem is the different level ofsupport for common libraries and frameworks across devices, such as .NETversions, where a headset supports legacy versions at major releasesbehind mobile devices, resulting in access to a reduced feature set. Atleast one of the embodiments described in accordance with the teachingsherein provides a technical solution by having a differentimplementation of functions, such as WebSockets.

In another aspect, another technical problem is the difficulty ofcoordinating device/platform specific rendering. For example, remotebrowsers have a virtual experience as they do not view physical modelsin the local space, and different shaders are needed on differentdevices. At least one of the example embodiments described in accordancewith the teachings herein provides a technical solution by havingdifferent display options based on context, when no physical model ispresent, to visualize the base anatomical model as well, and by usingwireframes on virtual models, which provide a better display of surgicalplans.

In another aspect, another technical problem is the difficulty ofreconciling different coordinate system types with different renderingengines. For example, Unity™ uses left-handed coordinate systems, whileother rendering engines use right-handed coordinate systems. At leastone of the embodiments described in accordance with the teachings hereinprovides a technical solution by mapping coordinate systems back andforth across different rendering engines. For example, where a renderingengine uses a left-handed coordinate system, the positive x, y, and zaxes point right, up, and forward, respectively, and positive rotationis clockwise about the axis of rotation. Where a rendering engine uses aright-handed coordinate system, the positive x and y axes point rightand up, and the negative z axis points forward, and positive rotation iscounter-clockwise about the axis of rotation. Such a formal conventiondefines a 3D coordinate system (X/Y/Z). Where the convention used byvarious rendering engines is known, the coordinate mappings areaccomplished by creating inversion matrices which swap an axis todisplay correctly on the “new” coordinate system.

In another aspect, another technical problem is the difficulty ofbroadcasting and streaming device data so that multiple local and remoteclients can visualize content in a synchronized way. At least one of theembodiments described in accordance with the teachings herein provides atechnical solution by having data broadcasted to and shared acrossclients through WebSockets, which is a common standard withimplementations across different languages and frameworks. At least oneof the embodiments described in accordance with the teachings hereinprovides another technical solution by using time-stamped buffers at thedevice level to help synchronize data from remote clients in localapplications. The data is synchronized such that the data displays atthe same time and speed across various devices. For example, all thedevices display an image at the receive time derived from the same dataset, but possibly at different points of view, zoom levels, orperspectives (despite differences for optimization of images).

In another aspect, another technical problem is the difficulty ofco-registration of multiple devices and coordinate systems. At least oneof the embodiments described in accordance with the teachings hereinprovides a technical solution by (a) using reference coordinate framesthat are specific to each device/sensor; (b) collecting data of areference object present in each individual reference frame; and (c)utilizing measurement correspondences that enable co-registration ofcoordinate frames.

In another aspect, another technical problem is the difficulty ofreal-world deployment to institutions that have organizationalfirewalls, along with the associated security and privacy issues. Atleast one of the embodiments described in accordance with the teachingsherein provides a technical solution by ensuring that, at the systemlevel, security and privacy are used at the data store, encryption isused in storage and transit of data, anonymization of data is used, anddistribution of data during a session is not permanently stored but onlyexists in RAM for the duration of the session. At least one of theembodiments described in accordance with the teachings herein providesanother technical solution by providing an AR system that complies withdifferent institutions' security and privacy policies and uses VPNtunnels, while simultaneously allowing technical staff at eachinstitution control over privacy and security settings of the system.

Reference is first made to FIG. 1A, showing an example embodiment of anaugmented reality (AR) system 100 for multi-client broadcasting andstreaming. The system 100 provides a framework for AR applications withdata streaming and synchronization that can be deployed across multipledevices locally and remotely. The system 100 includes a server 110 and adatabase 150 where the server 110 can communicate with one or moreclient devices 170. The server 110 may include one or more computersthat operate as an HTTP server 112 and a WebSocket server 114. Clientdevices 170 may be, for example, mobile devices (e.g., tablets, phones),desktops, laptops, headsets, or projector systems. The client devices170 may have a processor (which can refer to a single processor, orcollectively to a dual processor or multiple processors). Theapplication 172 is a software program that may be deployed natively orthrough web standards conforming browsers supporting technologies suchas WebGL and WebXR. The application 172 is used to allow a user tooperate one or more of the AR methods described herein on their clientdevice. The application 172 may have a user interface (UI) for operationof these AR methods. The client devices 170 may operate software orotherwise communicate with the server 110 such that the client devices170 can be logically divided into a primary client device 170 and one ormore replicate client devices 170 a (only one of which is labelled inFIG. 1A for ease of illustration). For simplicity, reference to clientdevice 170 applies equally to replicate client device 170 a unlessspecifically referred to as the primary device, and vice versa.

An AR library 174 and the database 150 can be agnostic to the engine andrendering architecture of the applications 172 across client devices 170due to conformance to device application programming interfaces (APIs),platform software development kits (SDKs), data, and technologystandards. For example, one client device 170 may deploy the application172 on a tablet via Unity, a popular game engine, whereas another clientdevice 170 may be an iPhone that runs an application built via Apple'snative toolchain and ARKit.

The database 150 includes references and relations for: (a) data models152 used in AR applications, (b) user profiles 162 (shown as “Users162”) for access control and authentication of the users of the clientdevices 170, and (c) sessions 164 for synchronization of AR applicationsthat are used across multiple devices and locations.

In this example embodiment, the data models 152 used in the AR system100 include model sets records 154, plans records 156, recordingsrecords 158, and instruments records 160.

The model sets records 154 can be more generalized data models andinclude static surface and volume representations derived acrossdifferent imaging modalities such as, but not limited to, at least oneof computed tomography (CT), medical resonance (MR), positron emissiontomography (PET), and ultrasound (US), for example. These can spanacross multiple stages of imaging and intervention such as, but notlimited to, at least one of preoperative, intraoperative, postoperative,and ex-vivo, for example. The static surface and volume representationscan represent different physiological data such as, but not limited to,at least one of the normal anatomy and contours of structure anddisease, for example.

Alternatively, or in addition, the static surface and volumerepresentations can represent: surface anatomy of the skin; contours ofnormal bony anatomy and abnormal or morphological differences;disruptions or irregularities in bony anatomy (e.g., fractures of thebone, and tumors of the bone); surface and internal anatomy of softtissue (e.g., blood vessels, muscles, and nerves) in the normal andabnormal/diseased state; regions of interest; and critical structures tobe avoided. Surface and volume representations can also includenon-human or animal data, tools, and/or physical features of theenvironment (e.g., operating room table, operating room lights, fiducialmarkers, and operating microscope). When displaying critical structureavoidance by AR visualization, the intervention plan may include anintervention target and a critical structure surface or volume inproximity of the intervention target.

The plans records 156 include parametric information and geometricrepresentations regarding how a procedure is to be executed in referenceto data from the model sets records 154 such as contours of structureand disease. Examples of parametric information for a procedure include:lines defined by a point; direction vectors from an entry and targetpoint; planes defined by a point and a normal as fitted to a set ofentry and target points. Geometric representations can be used forrendering, such as four vertices and a quad that makes up the plane.

The recordings records 158 include dynamic information recorded throughconnected devices such as, but not limited to, at least one ofnavigation data of tracked tools, gesture information of users using atracked tool, annotations (e.g., audio, text), video, and sensorstreams, for example.

The data models 152 used for AR applications may adhere to FAIR dataprinciples (see, e.g., https://www.nature.com/articles/sdata201618):Findable, Accessible, Interoperable, Reusable. Alternatively, or inaddition, the data models 152 may be configured to adhere to other dataprinciples or standards. The FAIR data principles are described asfollows:

-   -   Findable: data is assigned a globally unique and persistent        identifier (ID) and is described using rich metadata;    -   Accessible: data is retrievable by the ID via standard        communication protocols with appropriate authentication and        authorization, and metadata can be retrieved independently of        underlying data;    -   Interoperable: metadata is represented in standard        data-interchange format, data assets (e.g., any other data) are        stored in standard formats, and metadata references other        metadata or data as appropriate; and    -   Reusable: metadata specifies a data usage license, metadata        adheres to community standards, and metadata describes data        assets through accurate and relevant attributes.

The data models 152 may adhere to FAIR guidelines through the followingimplementations:

-   -   Findability: a Universally Unique IDentifier (UUID4) is        generated and assigned to each data model 152, and each data        model 152 is paired with a detailed metadata;    -   Accessibility: REST API endpoints provide JavaScript Object        Notation (JSON) responses that are human and machine-readable,        metadata is retrieved independently of data model assets, and        only authorized users may access data model assets        (authentication is done via OAuth2 protocol);    -   Interoperability: API replies are in JSON, which is a        data-interchange format consumable by most languages and        platforms, and data model assets are stored in industry standard        formats (e.g., DICOM/Nifti for volume representations, and OBJ        for mesh representations); and    -   Reusability: metadata contains tag attributes that provide a        context for the data and can be queried against, metadata        specifies a license and conditions for data use, and metadata        specifies references and sources where data was used.

Advantageously, in at least one embodiment, the server 110 and thedatabase 150 of the system 100 can be used to manage the AR data andvarious user sessions by using metadata that adheres to FAIR dataprinciples.

The metadata JSON for a data model 152 may contain a number of fieldssuch as, but not limited to:

-   -   ID: a universally unique identifier for the data model 152;    -   Description: a detailed description of the underlying data and        procedures;    -   Date: the date that the data model 152 was created;    -   Correspondence contact: a contact person for inquiries regarding        data;    -   Nodes: a parent/child relationship between representations,        coordinate systems, and their transforms;    -   License: authorization and use permission information;    -   References: external references, including supporting        publications, institutional research protocol number, and where        data has been used;    -   ModelSets: resource paths (e.g., where the data is stored on a        server, cloud, or folder structure) for volume and surface        representations with corresponding SHA-2 and SHA-3 hashes;    -   Plan: a resource path for a plans record 156;    -   Instruments: resource paths with corresponding SHA-2 and SHA-3        hashes for surface representations of instruments records 160        used with the data model 152; and    -   Recording: a resource path of recordings records 158.        In some embodiments, some of these fields may not be used and/or        additional fields may be used.

The fields may also include: Tags: which are query fields that includefive subfields: (1) Site: anatomical site, (2) Pathology/Injury: typesof pathology and injuries present; (3) Intervention: type of procedure;(4) Modalities: underlying modalities included in the model sets record154, and (5) Recordings: a list of recordings records 158. The fieldsmay be used when, for example, the server 110 or a client device 170uses a query to find procedures.

In a particular implementation, real-time data is broadcasted throughWebSocket as JSON objects with associated metadata in the header field.The JSON object contains:

-   -   Name: a data event name for the data model 152;    -   Header: a metadata array descriptive of the message field for        the data model 152; and    -   Message: a data array corresponding to the event for the data        model 152.        Tool data, user commands, metrics, and evaluations are        broadcasted as a JSON object with a metadata header from a        client device 170.

As an example, osteotomies and needle guidance may have the followingencapsulation:

  {  “name”: “eval”,  “header”: [   “distancevalidity”,  “pitchvalidity”,   “rollvalidity”,   “distance”,   “pitch”,   “roll” ] ,  “message”: [1, 1, 1, 1.11, 3.57, 4.91] } {  “name”: “eval”, “header”: [   “distancevalidity”,   “distanceToLineValidity”,  “angleValidity”,   “distance”,   “distanceToLine”,   “angle”  ] , “message”: [1, 1, 1, 2.10, 2.23, 9.71] }

Client devices 170 may also utilize message queues for inter-processcommunication between connected hardware devices and data sharingbetween APIs such as with navigation hardware, microphone, or gestureinterfaces.

Advantageously, in at least one embodiment, the data models 152 can beused to provide visual feedback. The data models 152 include datarelated to anatomy, pathology, instruments, and surgical plans generatedpost patient image acquisition. Typical patient image acquisitionsinclude volume images (e.g., a series of DICOM files)—surgical planningdata, segmentations, surface models, and instrument representations usedin AR are not stored in a standardized way (because most patient imagesare interchanged with a picture archiving and communication system(PACS) server, which only manages DICOM files). Surgical planning datamay be parametrically defined, which includes at least one of entry andtarget points, line definitions, and segmentations (e.g., manual,semi-automatic, or automatic from contouring over patient images), whichcan lead to segmented surface models that are representations of theanatomy and an underlying pathology. Instrument representations may begenerated through 3D modelling or CAD, or may be reversed-engineeredthrough 3D modelling and CAD from a reference surface scan. The datamodels 152 used in AR can also be used to provide visual feedback wherethe spatial and temporal context is better conveyed by the alignment ofthe visual feedback to physical space. Furthermore, tracked instrumentsprovide real-time qualitative (visual) and quantitative feedback(metrics) with respect to the surgical plan.

The model sets records 154 may include representations for surface orvolume rendering that are directly derived from imaging and segmentationacross various stages. The volume representations may be stored intraditional medical image formats such as Nifti and anonymized DICOM,where headers encapsulate spatial information in origin, orientation,spacing, and extent of the data volume, as well as the voxel scalar datatype. In addition to volumes that are directly derived from imagingmodalities, volumes may also come from planning, such as prescribedradiation dose volumes in radiation therapy or contours or pointannotations of various anatomical structures and disease. Volumerepresentations can be visualized via direct volume rendering on theapplication 172 by using various visualization techniques such asray-casting and texture-based methods. Two-dimensional (2D) slices canbe specified according to indices across the imaging axes or via a pointand normal to interpolate scalar values on an oblique imaging plane.

In at least one embodiment, the 2D slices are specified using a DICOMseries. The DICOM series may contain origin, spacing, and direction metainformation in the DICOM tags. For each DICOM file in the series, theremay also be a slice location index in the DICOM tag indication where theslice is along the axis of the series. DICOM files belonging to the sameseries can be stacked to form a 3D matrix of scalar values (voxels).Once stacked in a volume, slices in the other two axes may be retrievedby fixing a location in the axis of interest and retrieving the 2Dsubmatrix. Furthermore, once the 3D matrix of a voxel is formed, obliqueplanes (i.e., planes that are not in alignment with the axes of thevolume) can be defined, the intersecting voxels of an oblique plane canbe determined, and scalar values can be interpolated.

Surface models can be created from volumetric imaging throughapplication of marching cubes to generate polygonal meshes. Masking canbe applied on the volume prior to applying marching cubes to specifyregions of interest (ROI) and better delineate structure. The marchingcubes can also be used on contour volumes to generate their surfacerepresentations. The polygonal meshes can result from these operationsby application of marching cubes. The polygonal meshes may also beconstructed via physical tools such as digitizer pens tracing outanatomy (e.g., across phantoms, cadavers, patients) or digital tools toedit meshes and define regions and patterns. The surface models can bestored in standardized file formats such as OBJ geometry definitions(e.g., stored as .OBJ files).

Face normals can be calculated by application of the cross product onface edges. Winding order consistency (e.g., ordering of vertices foredges) can be checked by propagating through neighboring faces andcomparing ordering of vertices in shared edges. Winding orderconsistency ensures that surface normals are oriented in the samedirection. Vertex normals can be calculated by summing the normals offaces that share the same vertex. Consistent normal orientation canensure proper rendering of the surface representation and ease ofcorrection (e.g., reversing normals across the whole model). Facenormals and vertex normals can be used in the surface models forrendering, where lighting models depend on the normal of a face/vertexand the camera direction, and the angle between a surface and a lightsource affects illumination at the face/vertex. More specifically, facenormals may be important for rendering meshes. For example, face normalsfacing outwards show the material (e.g., color, light reflectiveproperties, transparency) facing outwards towards the user. Face normalsfacing inwards, however, put a material on the inside of the object,which causes the object to look strange and not realistic to the user.

The plans records 156 can be stored as JSONs where fields specifyparameters for the planning geometry. Types of plans records 156 includesets of line trajectories for needle procedures and sets of cuttingplanes for geometric resection. The plans records 156 can be generatedthrough diagnostic viewing and planning software in reference to patientimaging. Line trajectories may be determined by an entry point and atarget point. The cutting plane may be defined by a set of entry/targetpoints, then the plane may be fitted to a set of points.

The recordings records 158 can be stored as JSON files, with recordeddevice data stored in an array according to their device channel field(e.g., from which device the data originated). Data streams that arerecorded can be synchronized, where each measurement corresponds to aclock tick of a controller that is used by the client devices 170. Anautomation track can be created that corresponds to events,interactions, and parameter changes captured during a recording in arecordings record 158.

The instruments records 160 include surface representations of variousclinical tools and instruments such as, but not limited to, probes,interventional tools, surgical tools, and biopsy tools, for example. Thetools may include tweezers, forceps, scissors, bone instruments,surgical and vascular clips and clamps, scalpels, retractors, woundclosure systems, and vascular access instruments. Different surfacescanners (e.g., depth cameras or lasers) can be used to scan theseobjects (i.e., tools and instruments) and create a point cloud thatrepresents the surface of the object. These point clouds can then beconverted into a mesh.

The user profiles 162 can contain account, authentication, and accessdata that is used for auditing users of the system 100.

The sessions 164 can be allocated dynamically to host real-timebi-directional communication between the server 110 and multipleconnected client devices 170 via WebSockets. A user having a userprofile 162 may create a session 164 with a password and distribute toother users with their respective user profiles 162 for login.

As previously mentioned, the server 110 may include one or morecomputers that operate as the HTTP server 112 and the WebSocket server114. The server 110 may be, for example, a physical server, a virtualserver, or a shared server. The HTTP server 112 and the WebSocket server114 may be implemented, for example, as their own separate physicalservers or modules on the server 110.

The HTTP server 112 can expose the API endpoints, which may includerequests for data models 152 (e.g., model sets records 154, plansrecords 156, recordings records 158), user profiles 162, and sessions164. For example, the data model endpoints can allow HTTP methods,including the GET (e.g., GET datamodels), PUT (e.g., PUT datamodel), andPOST (e.g., POST datamodel) operations. The GET datamodels operation canbe used to retrieve a complete list of authorized data models 152 fromthe database 150. The GET datamodels operation can specify tagsincluding site, intervention, modality, recording, and/orpathology/injury for queries for data models 152 that match specifiedtag fields. The GET datamodels operation can also specify an ID to getmetadata for a specified data model 152 corresponding to the ID wherethe metadata includes data asset paths and hash values. The PUTdatamodel operation can specify an ID and a supplied recording in orderto update a specific data model 152 with the supplied recordings record158. The POST datamodel operation can be used to upload a new data modelarchive including all assets with accompanying metadata.

The user endpoints can be used to authenticate users based oncredentials, in order to enable the querying and retrieval of authorizedcontent. For example, the user endpoints can utilize the POSToperations: (1) POST user to create a new user profile 162 with asupplied name and password, and (2) POST login user to authenticate auser based on credentials. The user endpoints can also utilize GEToperations to retrieve a list of users according to supplied queryparameters such as by name, organization, and session. The PUToperations allow updates of user information and settings. The DELETEoperation allows the deletion of users.

The session endpoints can be used to help manage WebSocket communicationacross multiple connected client devices 170. The session endpoints canutilize the POST operations: (1) POST session to create a session 164with a supplied name and password, and (2) POST login session toauthenticate a user based on credentials and session access. The GEToperation allows retrieval of available sessions by a user's sessionhistory, session the user is invited to, and access credentials. The PUToperation allows updates of a session such as user invite lists,referenced data models, settings, etc. The DELETE operation allows thedeletion of a session.

The WebSocket server 114 can manage real-time broadcast and streaming ofdynamic information across client devices 170 in the same session. Forexample, the WebSocket server 114 can synchronize device data streamsacross the same broadcast and listening channel ID within a session 164.The WebSocket server 114 can host multiple sessions 164 which clientdevices 170 may connect to. Each session 164 can represent a shared ARexperience where the underlying data models 152 are shared by varioususers across the connected client devices 170.

The client devices 170 can come in many different forms andtechnologies. The client devices 170 can host the AR library 174 andapplication 172 (native or web). The client devices 170 may stream datafrom locally connected devices and hardware or via the WebSocketchannels from a broadcasting client in the same session 164. The primaryclient device 170 denotes the client device that is locally connected tothe devices and hardware providing real-time data streams.

In at least one embodiment, a user of the primary client device 170 logsin and creates a session. The user then selects reference data models152 for the session and invites other users to join the session. Areference data model 152 is linked to the session and retrieved by theclient device 170. The client device 170 loads the data model 152 andsets up the rendering scene and data model representations. Users ofreplicate client devices 170 a can join a session they are invited toand can receive their data streams via device channels throughWebSocket. Locally connected data devices and hardware are initializedand set up via their API. Data streams (locally or remote via WebSocket)from connected devices are received and used to update objectrepresentations and computations. Client devices 170 include devicessuch as, but not limited to, mobile devices, AR headsets, VR headsets,projector systems, and web browsers (such as laptops and tablets), forexample. Accordingly and advantageously, in such embodiments, the system100 provides sessions for a shared AR experience across multiple clientdevices 170, both local and remote.

Mobile devices include phones and tablets, where the camera and inertialmeasurement unit (IMU) sensors of these devices can be used to providetracking and pose estimation between physical space and virtual space.Mobile devices can also support touch screen interaction, audiorecording and playback, and communication endpoints such as Bluetooth,WiFi, and LTE. Current and future generations of Android and iOS devicessupport AR capabilities at the device and platform level.

Headsets are capable of providing spatial interaction, reconstruction,tracking, and pose estimation between physical space and virtual spacevia a comprehensive suite of sensors (e.g., accelerometers, gyroscopes,RGB+Depth cameras, microphones). Headsets can use stereoscopic renderingand semitransparent displays to merge virtual content with the realworld. Headsets can also support user feedback via machine learningenabled interactions through voice and gestures interpreted through thesensors. Wireless communication capabilities of the headsets includeBluetooth and WiFi.

Projector systems can fuse virtual models with the real world byprojecting the render window directly on a physical field. The projectorcan be treated as an inverse pinhole camera, where the virtual camera ofthe render window is matched to its intrinsics (e.g., optical center,focal lengths, and field of view) so that overlay between virtual andphysical content can be spatially registered and visualized accurately.This may enable, for example, matching of a physical object and itsvirtual representation in location, orientation, and scale. Outside-intracking can enable tracking of pico-projectors and updating of thevirtual camera based on tracked poses.

Most modern browsers already support HTTP2 protocols as well asWebSocket. A connected camera device may provide a real-world stream foran AR overlay. Client applications can run in the browser utilizing webtechnologies such as WebGL and WebXR. Applications that run in standardsconforming browsers are inherently cross-platform and can run acrossdifferent devices, including mobile, headsets, desktops, and laptops.

Client device applications 172 can be built and deployed natively acrossdevices, with common engines such as Unity or Unreal Engine, or withcross-platform browsers conforming to web standards such as WebGL andWebXR. The application 172 can consist of setup, update, and renderloops, the implementation details of which are dependent on the choiceof engines and SDKs used. In setup, data assets can be loaded to createtheir graphic primitives and representations, such as instantiation ofvertices and faces and setting material properties such as specular,diffuse, and ambient color values. Virtual cameras can be set up tomatch the calibrated parameters of corresponding client device camerasto enable spatially accurate real-virtual overlay. In update-renderloops, data from input devices can be used for computation and updatingof position and orientation of data model graphic representations.

In at least one implementation, applications 172 are created using agame engine (such as Unity). For example, an application 172 is codedfor both Android and iOS devices through Unity, creating one application172 for both devices. Device-specific SDKs can then deploy theapplication 172 to the devices. Alternatively, device-specificapplications 172 can be written in a more native way without the use ofthe game engine, but they can only then use the device SDKs and not anyof the extra SDKs provided through a game engine. For example, separateapplications 172 are coded for an iOS device using Xcode and for anAndroid device using Android studio, creating applications 172 that showthe same information on the different devices.

The AR library 174 provides endpoints for the client application 172that can, for example, acquire and set up models, broadcast and streamdata including updated transforms of dynamic objects that are tracked,configure devices and track settings, and evaluate output metrics. TheAR library 174 includes various software code organized into a set ofclasses or modules (i.e., classes that facilitate similar functions andprocessing), not limited to a single programming language inimplementation. For example the various modules include an HTTP clientmodule 176, a WebSocket client module 178, a registration module 180, acontroller module 182, a devices module 184, a tracks module 186, ametrics module 188, a shaders module 190, and a classifiers module 192.

The HTTP client module 176 can handle requests/replies to the REST APIendpoints of the HTTP server 112. These include authenticating and loginof the user, querying and fetching of data, validation of fetched dataagainst hashes, and session management. In some implementations,frameworks provide implementations of HTTP protocol out of the box orvia third-party libraries.

The WebSocket client module 178 can broadcast and listen to other clientdevices 170 in device channels in a session 164. Device channel datastreams can be sent and received as JSON strings. Recipient devices canparse the JSON string to extract the data frame, which can be sent toits corresponding track for propagation. The tracks module 186 cangenerate tracks that are agnostic to whether devices are physicallyconnected, through WebSocket, or a loaded playback.

Advantageously, in at least one embodiment, the system 100 employsclient devices 170 not only to display AR data, but also for tracking(e.g., of tools, cameras, hands, fingers, gestures), particularly incombination with external sensors for spatial computing andgesture/voice recognition, as well as providing real-time feedback ofnatural user interaction and instrument measurements relative to anatomyand plan. Client devices 170 may be connected to each other (e.g.,wirelessly or through cabled Internet) through a session that enablesdata to be streamed across devices.

The registration module 180 can ensure that content is synchronizedspatially and temporally across data and devices. The registrationmodule 180 can provide functionality to co-register via spatial featurecorrespondences and manage data and devices across multiple coordinatesystems by the use of scene graphs.

The controller module 182 can set up and activate devices that caninteract in a session through a vendor device API, manage tracks toreceive, record, and process device data streams, and synchronize dataon the client device. The API can enable, for example, initialization,activation, and configuration of settings programmatically. Thecontroller module 182 can use a high-resolution clock where each tickcorresponds to updates using the devices module 184 and the tracksmodule 186. The clock may be an operating system level timer that can beused to timestamp received data, which are stored in buffers and can beinterpolated or queried after for synchronization. As devices often havetheir own frame rate, a global clock can be used to synchronize dataframes and help manage recording and playback of multiple data streams.

The devices module 184 can be used to provide data streams forconsumption through a device API and exchange to the server 110 and withother client devices in the same session 164 via WebSocket. Each datastream message may include an identifier and an array of values. Thevarious devices that interact with the server 110 may have their owndevice and data API from a vendor for initialization and data streaming.The devices may obtain data corresponding to hardware such asmicrophones, IMUs, navigation systems, eye trackers, spatial computingdevices, video and camera devices, haptics, and touch-screens on mobileclient devices 170, and package the data into the data streams. Thedevices may be on-board or external, communicating with the clientdevice 170 via a physical link or through WebSocket.

IMUs can be used for pose estimation in AR applications and typicallycontain a minimum of 6 degrees of freedom (DoF). IMUs can streammeasurements from an axis-aligned 3-axis accelerometer and a 3-axisgyroscope used to estimate relative position and pose (one view from thenext).

Micro-electro-mechanical-systems (MEMS) sensors may be attached tocamera-based devices to supplement pose estimation with computer visiontechniques. They may also be attached rigidly to the body to capturedynamics, such as capturing motion of an anatomical structure and adisease in correlation with movement such as breathing.

Microphones can be used to provide audio data to enable audio-to-textdictation, recording of audio clips for annotation, voice control, andcommunication with other connected users.

Navigation systems, such as active or passive optical tracking, can beused to provide accurate and precise outside-in tracking across a largeworking volume. Sensors may be attached to client devices 170 or toolsto enable optical tracking and guidance.

Eye trackers may be attached to headset devices or worn independently bythe user along with a world-facing camera. Eye trackers can be used toprovide eye tracking data to enable interactivity and manipulation ofvirtual models as well as gaze measurements which may be used inassessment of learners across tasks or to evaluate cognitive load.

Spatial computing devices may be attached rigidly to client devices 170to provide inside-out tracking and gesture interaction. Spatialcomputing devices come in a variety of technologies, such as RGB-Depthcameras (structured light, time-of-flight) and wide-angle stereoscopicsystems coupled with IMUs.

Video and camera systems can be used to enable different imagingmodalities such as but not limited to endoscopy and ultrasound, forexample. A video capture device can also be used to interface with aclient device and stream video data (e.g., for remote advice andmonitoring) through modern digital communication links.

Haptic devices can be used to provide feedback based on biomechanicalmodels matched with surface representations and tracked instruments. Thedynamics of a tracked instrument's position and biomechanical model canbe used to modulate an output signal to the electromechanically coupledhaptic device to produce feedback for the user. The biomechanical modelsinclude, for example, reference deformation and physics models, such asin Simulation Open Framework Architecture (SOFA). Surface deformationmodels can be tied to graphic primitives such as vertices or faces of abody's surface geometry then monitored and tracked with a depth camera(e.g., RGB-D).

Advantageously, in at least one embodiment, the system 100 providestracking of surgical instruments, which can be fused with AR content andphysical models. For example, suppose one part of a tool is tracked withoutside-in or inside-out tracking. Since the system 100 knows thelocation and orientation of this part of the tool (e.g., the end), otherparts of the tool can be calculated from this position (e.g., the tip ofthe needle or the end of the blade). The system 100 can be set up (orprogrammed) so that different AR content is viewed at these locationswith the correct orientation, such as a plane at the end of the tool togive the user feedback on the blade position and orientation to confirmit is correct before proceeding with the cuts. The application 172 canfacilitate the update and render loop. A camera on the client device 170can provide data that is used to facilitate inside-out tracking. Anexternal tracking camera can provide data that is used to facilitateoutside-in tracking.

The tracks module 186 uses memory blocks to buffer and filter datastreams from devices (e.g., live as connected physically, through aWebSocket client, or playback from recording). A buffer is a memorywindow that stores incoming samples with time stamps. Incoming data fromdata streams can be pushed into the buffer. Stored values can be usedfor interpolation based on controller clock tick for synchronization.Buffered data can also be used for smoothing or prediction via movingaverage filters or Kalman filter.

Advantageously, in at least one embodiment, the system 100 uses tracksto buffer and filter device data for broadcasting and synchronizationacross multiple clients in a session.

The metrics module 188 contains software code for evaluators that may beused on static or dynamic data to generate quantitative output forfeedback and guidance in virtual walkthroughs, simulations, or livecases. Real-time data may be live from connected devices or may berecorded from the devices. Examples of real-time data include positionand orientation of tracked surgical instruments (e.g., needle tipposition and orientation, plane of cutting saw or osteotome, drill tipposition and orientation, orientation and position of surgicalplates/screws, and depth of cut or movement), or video streams fromsources such as ultrasound or endoscopy systems.

Advantageously, in at least one embodiment, the system 100 providesreal-time feedback of tracked surgical instruments, metrics module 188relevant to a procedure or intervention, and a scoring assessment.

The shaders module 190 can be useful for AR rendering in a surgicalcontext by providing real-time data without cluttering the visual field,which may be accomplished by computing visualization for vertex andgeometry graphic primitives of a model object such as, for example, onlyvisualizing the outline of an object rather than the entire virtualobject. The shaders module 190 can contain OpenGL Shading Language(GLSL) and High-Level Shading Language (HLSL) implementations fornon-photorealistic rendering. These include outline and silhouetteshaders that can be applied to surface representations of anatomicstructures, disease contours, and margins.

In at least one implementation, the shaders module 190 can adjust thetransparency of objects so what is underneath the object can be seen, ordisplay regions like a 2D outline with regions encircled by the outlinebeing empty. The shaders module 190 can also turn the visibility ofparticular objects off if the user felt it was no longer necessary tosee it, but allow the user to turn the object back to being visible atany point.

The classifiers module 192 can be used in AR rendering as well. Theclassifiers module 192 can contain pre-trained machine learning modelsthat may be used across device data streams. These include, but are notlimited to, at least one of ensemble models for hand gestures (e.g.,adaptive boosting, decision trees, support vector machine (SVM), naïvebayes, random forest), motion localization across video frames, andsingle shot multi-box detectors for object detection. Training data ofleft- and right-hand gestures can be collected where each trainingsample for a hand is a feature vector of, for example, 28 values,including finger and joint positions, direction vectors of fingers andjoints, and palm position and normal. Training can be performed viamultiple classifiers, including decision trees, SVMs, adaptive boostingnaïve bayes, and random forest. An aggregated classifier can pass inputfeature vectors to trained classifiers, with the majority result beingstored in a circular buffer. The majority result of the circular bufferis the classification result that can be used by the system 100.

Referring now to FIG. 1B, shown therein is a block diagram of an exampleembodiment of a server 120 that can be used with the AR system 100 ofFIG. 1A. The server 120 may operate as the HTTP server 112, theWebSocket server 114, or both. The server 120 may run on a singlecomputer, including a processor unit 124, a display 126, a userinterface 128, an interface unit 130, input/output (I/O) hardware 132, anetwork unit 134, a power unit 136, and a memory unit (also referred toas “data store”) 138. In other embodiments, the server 120 may have moreor less components but generally function in a similar manner. Forexample, the server 120 may be implemented using more than one computingdevice.

The processor unit 124 may include one processor. Alternatively, theremay be a plurality of processors that are used by the processor unit124, and these processors may function in parallel and perform certainfunctions. The display 126 may be, but not limited to, a computermonitor or an LCD display such as that for a tablet device or a desktopcomputer. The user interface 128 may be an Application ProgrammingInterface (API) or a web-based application that is accessible via thenetwork unit 134. The network unit 134 may be a standard network adaptersuch as an Ethernet or 802.11x adapter.

The memory unit 138 may store the program instructions for an operatingsystem 140, program code 142 for other applications, an input module144, an output module 146, and the database 150. The programs 142comprise program code that, when executed, configures the processor unit124 to operate in a particular manner to implement various functions,tools, processes, and methods for the system 100.

In at least one embodiment, the AR system 100 allows real-time guided ARintervention (e.g., surgery) using metrics. Referring now to FIG. 46 ,shown therein is a flow chart of an example embodiment of a method ofguiding AR intervention 4600 in the AR system 100 of FIG. 1A. Method4600 provides steps (which may or may not occur in an order, and some ofwhich may be processed concurrently) that may be carried out in whole orin part to guide AR intervention using the server 110, the primaryclient device 170, and a replicate client device 170 a. The primaryclient device 170 and the replicate client device 170 a each have theirown processors and input devices that can generate real-time input data.

At 4610, the primary client device 170 receives model sets, anintervention plan having an intervention field, and session informationabout a session related to the AR intervention from the server 110.

At 4615, the primary client device 170 receives real-time input datafrom the input device of the primary client device 170. The real-timeinput data may include tracked input device information such as pose andposition, as well as video and sound if the first input device has thatcapability. The input device may include an instrument (e.g., anosteotome or scalpel) and a tracker camera tracking the instrument andproviding the pose/orientation data for the instrument.

At 4620, the processor of the primary client device 170 generatesmetrics by determining an evaluation of an execution of the interventionplan by comparing the intervention plan to the real-time input data. Themetrics may be selected based on any of the metrics described herein orother appropriate metrics.

At 4625, the primary client device 170 displays real-time graphics basedon the generated metrics that are spatially displayed over theintervention field. The real-time graphics may provide feedback ondeviations between the tracked tool and the planned intervention.

At 4630, the primary client device 170 receives real-time status datafrom the server 110 about a replicate client device 170 a connected tothe server 110 after the replicate client device 170 a joins thesession. The replicate client device 170 a can be used, for example, forremote interactions.

At 4635, the primary client device 170 sends the real-time input datathrough the server 110 to the replicate client device 170 a within thesession. The replicate client device 170 a may receive the real-timeinput data and render a scene. Remote users may then view and gaininsight into the scene that is seen by the user of the primary clientdevice 170.

At 4640, the primary client device 170 sends the metrics and theevaluation computed from the intervention plan, through the server 110,to the replicate client device 170 a within the session.

In at least one embodiment, the AR system 100 allows remote observationof a real-time guided AR intervention (e.g., surgery) using a replicateclient device 170 a. Referring now to FIG. 47 , shown therein is a flowchart of an example embodiment of a method of remotely observing aguided AR intervention 4700 in the AR system 100 of FIG. 1A. Method 4700provides steps (which may or may not occur in a certain order, and insome cases some of which may be processed concurrently) that may becarried out in whole or in part to guide and/or observe AR interventionusing the server 110, the primary client device 170, and a replicateclient device 170 a. The primary client device 170 and the replicateclient device 170 a each have their own processors and input devicesthat can generate real-time input data.

At 4710, the replicate client device 170 a receives the model sets, theintervention plan, and the session information about the session relatedto the AR intervention from the server 110.

At 4715, the replicate client device 170 a receives the real-time inputdata, the metrics, and the evaluation broadcasted from the primaryclient device 170.

At 4720, the replicate client device 170 a displays real-time graphicsbased on the model sets, the intervention plan, the real-time inputdata, the metrics, and the evaluation.

Method 4600 and method 4700 can be combined, in which case the steps ofmethod 4600 may be considered the primary client device stage and thesteps of method 4700 may be considered the replicate client devicestage.

Additionally, method 4600 and method 4700 may be used by a plurality ofreplicate client devices 170 a. When there is more than one replicateclient device 170 a, references to a replicate client device 170 a inthe steps to method 4600 and method 4700 can be interpreted as referringto one or more replicate client devices 170 a. In such a case, at anypoint during the method 4600 or the method 4700, additional replicateclient devices 170 a may join or leave the session, such that at anygiven instant, there may be zero, one, or more replicate client devices170 a connected to the server 110.

In at least one embodiment, the AR system 100 allows real-time remotementoring of the guided AR intervention. The remote mentoring can beaccomplished by carrying out additional steps to those of method 4600.These additional steps are described as follows. (1) The replicateclient device 170 a receives real-time input data from the input deviceof the replicate client device 170 a. The real-time input may helpprovide instruction and context from expert to novice. This may include,for example, selection of surface model regions, audio for vocalinstruction, and/or tracked tool data in their local setup (e.g., theexpert may have a 3D printed replica of the case, which theirinstruments are registered to and can demonstrate how to bestapproach/position the instrument). (2) The replicate client device 170 asends this real-time input data (received in the previous step) throughthe server 110 to one or more additional replicate devices connected tothe server 110 and the primary client device 170. (3) The primary clientdevice 170 receives this real-time input data (sent in the previousstep) from the server 110. (4) The primary client 170 displays real-timegraphics based on the real-time input data (received in the previousstep) that originated from the replicate client device 170 a.

Referring now to FIG. 2 , shown therein is an example embodiment of amulti-client configuration 200 for a WebSocket server 114 connected toclient devices 170 in the AR system 100. The server 110 can provide RESTendpoints to client devices 170 for data exchange, synchronization, andstreaming. The HTTP server 112 can provide endpoints for query anddelivery of content, user authentication, and management of sessions212. The WebSocket server 114 can enable multi-client broadcast ofreal-time data across device specific listening channels 214 (alsoreferred to as device channels 214). The server 110 may be localized todifferent network sizes, such as those run on a computer on a siloed LANand Wi-Fi SSID (operating room), across a hospital network, or deployedin the cloud so client devices 170 may connect remotely from differentgeo-locations. The client devices 170 may connect via a WebSocket clientmodule 178 to a session 212 served or broadcast by the WebSocket server114. The client devices 170 in the same session 212 may subscribe to thesame device channels 214 where device data frames are broadcast andstreamed.

Advantageously, in at least one embodiment, the system 100 utilizes theserver 110 to store, retrieve, and update data models 152 (e.g., rawdata, metadata), and broadcast real-time data across client devices 170,which may come from instruments and devices (e.g., video, two-wayaudio). The data and metadata can further adhere to the FAIR principlesdescribed above.

In at least one embodiment, the processors of each of the client devices170 can generate a chat window that enables communication between allconnected client devices 170. The chat window contains a field to viewpreviously sent messages and an input field that allows for a message tobe created. When the submit button is selected, the message contained inthe input field is broadcast to all client devices through the server110. An example layout of the chat window is shown in FIG. 36 . The chatwindow can be minimized and receive messages even while not visible.There can be a maximum number of messages that are stored on theindividual client devices 170 to minimize the amount of memory thisfeature requires. The messages may contain the username associated withthe client device 170 and the message. The client devices 170 can becategorized and a message color assigned to each category. This can bedone to improve readability of the messages.

In at least one embodiment, additional information relating to thevisible model set can be created and sent to all connected clientdevices 170. Specific client devices 170 can have the feature ofcreating text annotations that are then broadcast to all connectedclient devices 170. The procedure for creating a text annotation is toenable the feature and then select a 3D point on the model set. Forexample, a visually distinct object, such as a sphere, can then appearacross all client devices 170 at this position (e.g., as shown in FIG.37 ). This allows all client devices 170 to visually see that one mainclient is adding additional information. The processor of the primaryclient device 170 can then generate a window to input associated text(e.g., as shown in FIG. 38 ). There can be options to delete theannotation or save it. Deletion of the annotation causes the sphere tobe removed across all client devices 170. If the option to save isselected, the text is broadcast to all client devices 170, and thesphere changes color. This indicates that there is a message associatedwith the sphere mesh. The message can be viewed by selecting the sphere(e.g., as shown in FIG. 39 ).

In at least one embodiment, a primary client device 170 has the featureof controlling the view point of all connected replicate client devices170 a. This feature enables all connected replicate client devices 170 ato view the model set from the same position and orientation. Theposition data (e.g., in the form of a vector) and orientation data(e.g., in the form of a quaternion) of the primary client device 170 inregards to the physical model is calculated and then broadcast to thereplicate client devices 170 a. All replicate client devices 170 a havethe ability to view the model set independently from other replicateclient devices 170 a. This can be disabled, and the incoming positionand orientation data can be used to move and orient the viewpoint of thereplicate client device 170 a. An example of such control of the clientdevice viewpoint is shown in FIG. 40 , where the view of the primaryclient device 170 is broadcast so that connected replicate clientdevices 170 a have the same view.

Referring now to FIG. 3 , shown therein is an example of a scene graph300 for an outside-in navigation setup. A tracked reference is attachedrigidly to the body (e.g., adhesively to surface or anchored viascrews). A tracked reference is an object of known geometry withdesigned identifiable features. The tracker detect and extract featuresdetermine the transform (e.g., rotation and translation) of thereference in the tracker node coordinate system. The geometry andfeatures of a reference sensor or a reference marker is known a prioriby design such as infrared spheres with known engineered spacing betweeneach sphere or image marker with pre-computed scale and rotationinvariant image features. Detected and extracted features from atracking system are then matched to the features known a priori todetermine the transform via homography, perspective-n-point, or rigidregistration between 3D point correspondences. A registration transformmaps points in the physical space (the reference sensor) to the virtualspace (data models).

In at least one implementation, the tracked reference is a marker or acollection of infrared spheres that are detected by cameras. Theattachment point depends on the object. For tools, this can be the endof the object so that it does not affect the user's ability to use thetool, and it is always visible to the cameras. These markers can besecured using various methods such as clamps, tapes, or screws,depending on what the body is.

Scene graphs are used to represent spatial relationships between modelrepresentations, devices, and tools. A node in the graph represents acoordinate system, and an edge between two nodes defines the transformbetween them. The directionality of the edge denotes how the transformis to be applied for traversal. Going along the direction (wheredirectionality of the arrow is the forward transform and going againstthe arrow is the inverse transform) applies the forward transform, andgoing in the opposing direction applies the inverse transform. Thetransform is a 4×4 matrix containing the rotation and translation thatmaps a point from one coordinate system to the other.

In at least one embodiment, scene graph 300 represents an exampleembodiment of an outside-in navigation setup in which each nodecorresponds to a coordinate system in an outside-in tracking setup forclient device 170. The scene graph 300 represents a tracking setup forAR-capable devices. This setup provides outside-in tracking. Outside-intracking uses an external tracker to track sensors that are placed on aheadset or another AR device in use to determine pose of the device. Anexternal optical tracker with a large field of view can track active orpassive optical sensors fixed rigidly to the client device 170 orclinical tools. In at least one implementation, tracking of the headsetor mobile device is done using cameras that are placed around a room;the device itself does not calculate where it is but it might calculateits orientation depending on the device.

Node 310 represents the device coordinate system, which application 172receives data streams from. The data streaming device is rigidlyattached to the client device 170 and has a fixed transform to thecoordinate system of the client device 170. Node 320 represents thecoordinate system of the client device 170. Node 330 represents thecoordinate system of a sensor attached to the client device 170 trackedby an external tracker. The edge between nodes 320 and 330 representsthe transform to map a point in the client device coordinate system tothe sensor. Node 340 represents the coordinate system of a physical toolor instrument. Node 350 represents the coordinate system of the sensorattached rigidly to the physical tool or instrument. The edge betweennodes 340 and 350 represents the transform to map a point in the toolcoordinate system to the attached sensor. Node 360 represents theexternal tracker coordinate system. Edges between nodes 330 and 360 andbetween nodes 350 and 360 represent the transforms that map a point incoordinate systems of sensor node 330 and sensor node 350 to thetracker. Node 370 represents the coordinate system of a reference sensorattached to a physical body. Node 380 represents the coordinate systemof the virtual space and data model set. The edge between nodes 370 and380 represents the transform that maps points in the physical coordinatesystem of the reference sensor to the virtual coordinate system.

Following the scene graph and concatenation transforms of edgestraversed between source and destination nodes, the client device 170may then map coordinates of a tool (node 340) or device (node 310) todata model coordinates (node 380) and display the virtual-space imagesof the device and the tool. The application 172 may generate thevirtual-space images.

In at least one embodiment, the client device 170, having a processor,can carry out a method of outside-in tracking, the method comprising:receiving device image data at the processor from a first camera;determining device coordinates from the device image data using theprocessor; mapping the device coordinates to device sensor coordinatesusing the processor; mapping the device sensor coordinates todevice-tracker coordinates using the processor; mapping thedevice-tracker coordinates to device-reference coordinates using theprocessor; applying a first registration transform to thedevice-reference coordinates using the processor to display the devicein virtual space; receiving tool image data at the processor from asecond camera; determining tool coordinates from the tool image datausing the processor; mapping the tool coordinates to tool sensorcoordinates using the processor; mapping the tool sensor coordinates totool-tracker coordinates using the processor; mapping the tool-trackercoordinates to tool-reference coordinates using the processor;generating a virtual-space image of the tool by applying a secondregistration transform to the tool-reference coordinates using theprocessor; and displaying the virtual-space image of the tool on adisplay.

Referring now to FIG. 4 , shown therein is an example embodiment ofoutside-in tracking 400 used in osteotomy in which a tablet device isused with an osteotome that is tracked by an infrared optical tracker.An external system is used to track sensors that are attached rigidly totools and client devices 170. Dashed lines show the directions thatfollow the scene graph 300 of FIG. 3 . The external optical tracker isan infrared optical tracker 410. The client device 170 is a tablet 420having a camera 422, and an optical sensor 425 that corresponds tosensor node 330. The osteotome 430 corresponds to the tool node 340 withthe optical sensor 435 corresponding to sensor node 350. The transformsbetween the optical sensors 425, 435, the tablet 420, and the osteotome430 are offset transforms that align the coordinates of the camera 422(i.e., the origin of the camera corresponds to the device origin onmobile devices in the context of AR applications) and the coordinates ofthe osteotome 430 to their respective sensors. The infrared opticaltracker 410 uses a reference 440, which has an optical sensor 445 thatcorresponds to reference node 370.

In contrast to the traditional navigation setup, AR-capable devices mayuse inside-out tracking to provide tracking of reference sensors andpose estimation with an on-board device camera and IMUs. Additionalspatial computing devices can be used to provide tool tracking, as theycan provide a larger field of view, matching requirements of toolhandling in the surgical field. An example of a headset device with anattached infrared spatial computing device is shown in FIG. 6 (describedbelow). In that example, the client device 170 is the headset, and thedevice node 520 corresponds to the infrared spatial computing device.

Referring now to FIG. 5 , shown therein is an example embodiment of ascene graph 500 which represents a tracking setup adapted to AR-capabledevices for inside-out tracking. Inside-out tracking is where thesensors/camera are attached to the device (inside) and look out to theenvironment to calculate device position. This is contrary to outside-intracking, whereby external sensors detect the device and calculate itsposition relative to their fixed position. One or the other, or both,may be used in various AR/VR devices. A camera of the client device 170is used to track a reference sensor. A separate spatial computing devicewith a larger field of view (e.g., larger than the field of view of aheadset or mobile device) is used to track tools using active or passiveoptical sensors that are fixed rigidly to the client device 170 or toclinical tools. Each node of the scene graph 500 corresponds to acoordinate system for the inside-out setup of client device 170.

Node 510 represents the coordinate system of the tool or instrument.Node 520 represents the coordinate system of the spatial computingdevice (e.g., an RGB-D camera or a stereo infrared camera). The edgebetween node 510 and node 520 represents the transform between the toolor instrument and the spatial computing device where the position andorientation of the tool or instrument is computed by the spatialcomputing device. Node 530 represents the coordinate system of theclient device 170. The edge between node 520 and node 530 represents thefixed transform between the spatial computing device attached to theclient device 170. Node 540 represents a physical reference in the spaceor attached to a body. The edge between node 530 and node 540 representsthe transform between the client device 170 and reference as determinedby the client device camera and detected features of the referencematched to features known a priori. Node 550 represents the coordinatesystem of the virtual space and the data model 152. The edge betweennode 540 and node 550 represents the transform that maps points in thephysical coordinate system of the reference to the virtual coordinatesystem. Following the scene graph and concatenation transforms of edgestraversed between source and destination nodes, the client device 170may then map coordinates of the tool (node 510) to data modelcoordinates (node 550) and display the virtual-space images of thedevice and the tool. The application 172 may generate the virtual-spaceimages.

Referring now to FIG. 6 , shown therein is an example embodiment of aninside-out tracking setup 600 that can be used in osteotomy. A headset610 using an attached infrared (IR) camera 614 (or, for example, a pairof IR cameras) is used to track a reference plane 630, and a spatialcomputing device is used to track a tool, such as an osteotome 620. Thespatial computing device may be a dedicated RGB-D camera or stereocamera that captures information from the physical space to extractspatial information, for example, for tracking tools or features in theenvironment to extract a 3D pose estimation of the headset and/ordetected objects. The headset 610 includes a (e.g., RGB-D) camera 612and one or more IR cameras 614. The headset 610 corresponds to theclient device node 530. The osteotome 620 corresponds to the tool node510. The osteotome 620 has an optical sensor 625 attached thereon. Thetransforms between the optical sensor 625, the headset 610, and theosteotome 620 are offset transforms that align the coordinates of thecamera 612 (i.e., the origin of the camera corresponds to the deviceorigin on the spatial computing devices in the context of ARapplications) and the coordinates of the osteotome 620. The camera 612uses the reference plane 630, which corresponds to reference node 540.The reference plane is a physical version of an image marker, whoseimage features are computed a priori. The camera 612 can extract imagefeatures of video frames and match features to those computed a priori,where pose estimation of the camera 612 can then be computed viahomography.

In at least one embodiment, the client device 170, having a processor,can carry out a method of inside-out tracking, the method comprising:receiving tool image data of a tool at the processor from a firstcamera; determining tool coordinates from the tool image data using theprocessor; mapping the tool coordinates to device coordinates using theprocessor; mapping the device coordinates to client device coordinatesusing the processor; mapping the client device coordinates to referencecoordinates using the processor; generating a virtual-space image of thetool by applying a registration transform to the reference coordinatesusing the processor; and displaying the virtual-space image of the toolon a display.

The data models 152 used for AR applications (e.g., data models 380 anddata models 550) can be co-registered prior to insertion and indexing tothe database 150. This can be handled post image acquisition as part ofthe processing, segmentation, and optimization pipeline. Image volumesacross different imaging modalities can be registered via commonlandmarks or via image-based methods including cross-correlation andmutual information.

An example of such registration of image volumes can arise with CT/MRIimages of body parts. A CT or MRI image of a body part such as a face isscaled and translated based on common anatomic features. These may beintrinsic features such as a bony landmark or extrinsic features such asa fiducial marker, or a combination of these features in two dimensionsand three dimensions. Fusing or overlaying images from CT and MRI whereone or more common landmarks such as a bony prominence present in bothimages is used to align the images accurately and register the remaininginformation accurately in physical space (e.g., blood vessels and softtissue tumor from MRI are fused/registered with CT data, which is betterat bony reconstruction).

The exact scene graph configuration (e.g., scene graph 300 and scenegraph 500) in AR is dependent on the client device and trackingconfiguration. For inside-out tracking (see, e.g., FIG. 5 ), mobiledevices by themselves may rely on a camera and internal IMUs to providetracking and infer device pose relative to a reference. Additionaldevices and sensors, such as RGB-D cameras, may be attached as well tothe device for improved spatial reconstruction, sensing, and tracking oftools across a wide field. Many spatial devices are camera-based.Camera-based systems can be co-registered via relative pose calculationsthrough perspective-n-point, homographies, random sample consensus(RANSAC), and Kalman filtering post device camera calibrations.

Device camera calibrations can calculate the intrinsics, distortioncoefficients, and view angle of the camera. When virtual cameras inrendering pipelines are modeled as ideal pinhole cameras with zerodistortion and their optical center located at the center of the renderwindow, pixel mappings are calculated to map device camera frames tothat of an ideal pinhole camera, correcting for optical center offsetand distortion. Pose calculations of the calibrated ideal pinhole devicecamera can then be used to update the transform of the virtual camera,and overlay of data models 152 can be matched more accurately postmapping correction.

Referring now to FIG. 7 , shown therein is an example of posecalculation 700 of two camera devices viewing a common reference objectwith known coordinates and spatial points. Pose can be recovered throughperspective-n-point.

Given a reference object 730 with known spatial points matched to imagepoints in a corresponding frame from device 1 camera 710, the camerapose with respect to the reference object 730 can be calculated throughperspective-n-point. This can be repeated for additional attached cameradevices, such as device 2 camera 720. For the two-device setup, this cancreate the scene graph relationship in FIG. 8 (described below), wheredevice 2 camera 720 is rigidly registered to device 1 camera 710, wherethe registration transform is determined through the scene graphtraversal from device 2 camera 720 to device 1 camera 710.

Referring now to FIG. 8 , shown therein is an example of a scene graphequivalency 800 for two devices viewing a common reference object. Forcamera-based systems that are rigidly attached, the device registrationtransforms are fixed and relative pose can be calculated viaperspective-n-point or homography.

For reference planes with known spatial points, the camera pose relativeto the reference plane can be determined with matched image and spatialpoints through homography. The registration transform is then determinedthrough traversal of the scene graph.

In scene graph equivalency 800, a first registration transform T1 isapplied to the spatial points from device 1 camera 810 viewing areference 830. A second registration transform T2 is applied to thespatial points from device 2 camera 820 viewing the reference 830. Thetransform from device 2 camera 820 to device 1 camera 810 is thereforeT1 ⁻¹T2 (where T1 ⁻¹ is the inverse of T1).

Referring now to FIG. 9 , shown therein is an example of posecalculation 900 of two camera devices viewing a common planar objectwith known coordinates and spatial points. Pose can be recovered throughhomography.

Given a reference plane 930 with known planar points matched to imagepoints in a corresponding frame from device 1 camera 910, the camerapose with respect to the reference plane 930 can be calculated throughhomography. This can be repeated for additional attached camera devices,such as device 2 camera 920. For the two-device setup, this can alsocreate the scene graph relationship in FIG. 8 (described above), wheredevice 2 camera 920 is rigidly registered to device 1 camera 910, andwhere the registration transform is determined through the scene graphtraversal from device 2 camera 920 to device 1 camera 910.

Scene graph equivalency 800 may be used with pose calculation 900 in amanner similar to that used with pose calculation 700. Use of scenegraph equivalency 800 can be modified depending on the type of reference830 used. For example, for pose calculation 900, the registrationtransforms can be determined via homography and RANSAC from the matchedimage to plane points.

In at least one embodiment, the client device 170 can carry out a methodof co-registration of spatial devices using one or more of posecalculation 700, pose calculation 900, and scene graph equivalency 800.The method begins with the client device 170 receiving a first frame ofa reference object from a first camera. The application 172 determinesfirst image points from the first frame. The application 172 determinesa first camera pose by perspective-n-point or homography applied to thefirst image points matched to the first frame. The client device 170receives a second frame of the reference object from a second camera.The application 172 determines second image points from the secondframe. The application 172 determines a second camera pose byperspective-n-point or homography applied to the second image pointsmatched to the second frame. The application 172 combines the firstcamera pose and the second camera pose to co-register the spatialdevices.

For offset calculation between an attached sensor and a tool, certainmodifications may be required. Multiple measurements can be collectedbetween the sensor and an anchored reference point with the tip of thetool touching the reference point. The translation offset between thesensor and the tip of the tool can then be calculated throughoptimization of the system of equations across collected measurements.Alternatively, or in addition, a pre-calibrated pointer tool may be usedto touch the tool tip with the sensor acting as the reference.

Virtual and physical models may be registered using known spatial points(fiducials or anatomic landmarks), requiring a minimum of 3 commonpoints, or via surface methods including iterative closest point (ICP)and 4-point congruent sets (4PCS).

Registration with known spatial points may require identifying matchedpairs of points on the physical model and the surface or volumerepresentation of the virtual model. Physical model points may beselected via a tracked and calibrated pointer tool.

For surface-based registration, point clouds of the physical model fromspatial computing devices (e.g., stereoscopic, RGB-D, time-of-fightcameras) can be registered to the surface representation of a virtualmodel via 4PCS and ICP.

Object tracking of a known reference object or planar marker may beachieved through perspective-n-point or homography as mentioned above(see, e.g., the method of co-registration of spatial devices). In thecontext of object tracking, the device can be continuously updating itsrelative pose with respect to the tracked reference as opposed todetermining a fixed rigid relationship between two devices viewing thesame reference. The tracked object or planar marker may be of a fixedrelationship to a physical model, or the physical model may be trackeditself through feature correspondence.

Deformation tracking can be achieved via RGB-D cameras where RGB framepairs of matched image features have corresponding depth image values. Adepth image value corresponds to a point in the point cloud which isregistered to a vertex and polygons in the surface representation of thevirtual model. Thus, tracking point cloud changes can be used to computecorresponding mesh dynamics from frame to frame.

Inside-out tracking may be facilitated via client device APIs andplatform SDKs. These include ARKit on iOS devices and ARCore acrossGoogle devices.

For generalized camera-based tracking, a high-contrast image may be usedfor planar marker tracking, where reference image features are known apriori as spatial points on the plane. Matched spatial points and imagepoints in a frame may then be used to calculate camera pose viahomography.

Tracking of infrared reflective passive spheres can be accommodated byan attached spatial computing device with an infrared sensor. In astereoscopic setup, a circular Hough transform can first be applied tolocate marker centers. Then centers can be matched according to epipolargeometry. Matched centers can then be triangulated to determine centersin 3D. A sensor definition can be expressed as a distance graph betweenlabelled markers. Post triangulation, a distance graph may beconstructed and matched to the definition distance graph to extract amarker ID.

Outside-in tracking can be enabled by attaching passive or activesensors to client devices and tools.

Referring now to FIG. 10 , shown therein is an example of a controllerconfiguration 1000 of the controller module 182 interacting with thedevices module 184 and the tracks module 186. In controllerconfiguration 1000, the controller module 182 generates a clock tick1022 to propagate data 1042.

The controller module 182 can be responsible for setting up andactivating devices via the devices module 184, the management of tracksthrough the tracks module 186, and synchronizing data 1042 on the clientdevice 170. The controller module 182 can use a high-resolution clockwhere each clock tick 1022 corresponds to updates using the devicesmodule 184 and the tracks modules 186. As input devices 184 of a clientdevice 170 often have their own frame rate, a global clock on clientdevice 170 can be used to synchronize data frames and help managerecording and playback of multiple data streams.

Live client devices 170 can run simultaneously at different frame rates.Time-stamped data from the devices module 184 can be stored in a buffer1062 provided by the corresponding tracks module 186 of the clientdevice 170. This also corresponds to the data frame broadcast to devicechannels via WebSocket. For client devices 170 listening on theWebSocket device channel, the data frame can be parsed and then storedin the buffer of their respective device tracks. On a controller clocktick 1022, the closest data frame from the buffer 1062 can be propagatedthrough a filter chain 1064.

For recorded data, playback devices can simulate the output format of areal device with data parsed and snapped to controller clock ticks 1022.Unlike live devices, playback devices can propagate data based on thecontroller's clock.

The tracks are containers that buffer and filter a data stream fromclient devices 170 (live as connected physically, through a WebSocketclient, or playback from recording).

Every tracks module 186 can use a buffer 1062 that is filled by thetime-stamped data from its corresponding device. Each data frame in thebuffer 1062 can be a vector of values. On a controller clock tick 1022,the data frame from the buffer 1062 can be propagated through the filterchain 1064. The final filter output can be passed along to the ARapplication for consumption and to evaluate against desired metrics.

Filters that process input data frames can be chained to process devicedata in sequence. Common filters include moving average, Kalman filter,N-step delay, thresholding, and remapping filters via look-up tables andtransfer functions.

For video frames, filters include grayscale, motion detection, focusmeasure, screenshot, convolution, smoothing, sharpening, edge detection,circular Hough transform, and remapping filters.

Data streams in video tracks can be 2D arrays (e.g., video frames)rather than vectors.

Annotation tracks can be tracks that are generated through interactionwith the system 100 by the end user. These may be audio and dictation ascaptured through the microphone or gestures.

An automation track can capture all events and parameter changes snappedto the refresh rate and controller clock tick 1022.

This enables, for example, playback of the recording exactly as theinteractions, performance, and execution took place with the system 100.This functional utility mirrors automation tracks used in audioproduction systems (such as captured MIDI control parameters andvalues).

During playback, the controller module 182 can sequence events andactions as parsed from the recorded automation track. These includesetup and control of the playback devices and management of filterchains 1064 and parameters across tracks according to how they wererecorded in sequence.

In at least one embodiment, the client device 170 can carry out a methodof controlling the operation of devices module 184 and tracks module186, using controller configuration 1000. The method begins with thecontroller module 182 generating controller clock ticks 1022. Thecontroller module 182 receives a first plurality of input data from afirst input device having a first set of corresponding time stampsdetermined from the controller clock ticks 1022. The controller module182 receives a second plurality of input data from a second input devicehaving a second set of corresponding time stamps determined from thecontroller clock ticks 1022. The controller module 182 stores theplurality of input data in a buffer 1062 using its corresponding tracksmodule 186. The controller module 182 sends each of the plurality ofinput data to a filter chain 1064 at different controller clock ticks1022. The filter chain 1064 generates a first plurality of filter chainoutput of processed first input data and a second plurality of filterchain output of processed second input data. The controller module 182generates a plurality of data frames based on the first plurality offilter chain output and the second plurality of filter chain outputalong with the first set of corresponding time stamps and the second setof corresponding time stamps. The client device 170 sends each of theplurality of data frames and time stamps to the server 110 through theWebSocket client module 178. The client device 170 outputs each of theplurality of data frames to an AR application 172.

In at least one embodiment, the client devices 170 and the server 110can carry out a method of maintaining synchronization of real-time inputdata for broadcast and session logging functions. The method beings withthe primary client device 170 generating first clock ticks. The server110 receives a first plurality of data frames from the input device ofthe primary client device 170, which have a first set of correspondingtime stamps determined from the first clock ticks. The replicate clientdevice 170 a generates second clock ticks. The server 110 receives asecond plurality of data frames from the input device of the replicateclient device 170 a, which have a second set of corresponding timestamps determined from the second clock ticks. The server 110 combinesthe plurality of data frames based on the first plurality of data framesand the second plurality of data frames along with the first set ofcorresponding time stamps and the second set of corresponding timestamps, including the server time stamps at time of receiving the dataframes. The server 110 outputs the combined data frames and time stampsfor storage and/or broadcast.

In at least one embodiment, the client devices 170 and the server 110can carry out a method of synchronizing devices and tracks of amulti-user AR collaboration. The method begins with the primary clientdevice 170 storing the first real-time input data in a first buffer incorresponding first device tracks. A track (which may be part of thedevice tracks) may be, for example, a signal flow from an input device,starting from input data in the buffer. The primary client device 170generates first clock ticks. The primary client device 170 processes thefirst real-time input data in the first buffer through a first filterchain from the first clock ticks. A filter chain may be, for example, asequence of steps that transforms an input signal where the output of aprevious step routes to the input of the next step. The primary clientdevice 170 generates first data frames from the first filter chain. Theserver 110 receives the first data frames from the primary client device170 having a first set of corresponding time stamps determined from thefirst clock ticks. The replicate client device 170 a stores the secondreal-time input data in a second buffer in corresponding second devicetracks. The replicate client device 170 a generates second clock ticks.The replicate client device 170 a processes the second real-time inputdata in the second buffer through a second filter chain from the secondclock ticks. The replicate client device 170 a generates second dataframes from the second filter chain. The server 110 receives the seconddata frames from the replicate client device 170 a having a second setof corresponding time stamps determined from the second clock ticks. Theserver 110 generates combined data frames based on the first data framesand the second data frames along with the first set of correspondingtime stamps and the second set of corresponding time stamps. The server110 stores the combined data frames in the database 150.

In at least one embodiment, the client devices 170 and the server 110can use the plurality of data frames stored in the database. The methodbegins with the server 110 retrieving the combined data frames from thedatabase 150. The server 110 generates output clock ticks. Each of theclock ticks may be programmatically determined by checking against theoperating system (OS) clock. The OS clock is continuously running, and aclock tick corresponds to an event where a set frequency/interval matchan elapsed time on the OS clock. The server 110 extracts a primaryclient data frame and a primary client time stamp from the combined dataframes for the primary client device 170 corresponding to a currentoutput clock tick of the output clock ticks. The server 110 extracts areplicate client data frame and a replicate client time stamp from thecombined data frames for the replicate client device 170 a correspondingto the current output clock tick. The server 110 combines extracted dataframes of the primary client device 170 and the replicate client device170 a between server time stamps corresponding to the current andprevious output clock ticks. The server 110 broadcasts combined outputdata frames along with corresponding time stamps to the primary clientdevice 170 and the replicate client device 170 a.

In at least one embodiment, the client devices 170 and the server 110can carry out a method of controlling devices and tracks of a multi-userAR collaboration. The method comprises: generating clock ticks;receiving a first plurality of input data from the primary client device170 into a buffer; determining a first set of corresponding time stampsfrom the clock ticks; processing first buffer data from the buffer onthe clock ticks to generate a first plurality of data frames along withthe first set of corresponding time stamps; receiving a second pluralityof input data from the replicate client device 170 a into the buffer;determining a second set of corresponding time stamps determined fromthe clock ticks; processing second buffer data from the buffer on theclock ticks to generate a second plurality of data frames along with thesecond set of corresponding time stamps; sending the first plurality ofdata frames and the first set of corresponding time stamps to the server110; sending the second plurality of data frames and the second set ofcorresponding time stamps to the server 110; and outputting each of theplurality of data frames to an AR application.

Advantageously, in at least one embodiment, the system 100 providesrecording and playback of device data fused with AR content, includingannotations with spatial/temporal context, and feedback of device datawith respect to a plan (i.e., surgical, intervention, guidance, oreducation).

Referring now to FIG. 11 , shown therein is an example of metrics usedin needle guidance 1100.

The metrics module 188 contains software code for evaluators that may beused on static or dynamic data to generate quantitative output forfeedback and guidance in virtual walkthroughs, simulations, or livecases. Real-time data may be live from the devices module 184 ofconnected devices or recorded from the devices module 184. Examples ofreal-time data include position and orientation of tracked surgicalinstruments (e.g., needle tip and orientation, plane of cutting saw orosteotome), or video streams from sources such as ultrasound orendoscopy systems.

Surface or volumetric data may be collected after execution of aprocedure to co-register with pre-op or intraoperative datasets andsurgical plans. These may include surface models of the ex-vivo specimenand patient as captured by a spatial computing device (e.g.,stereoscopic cameras, RGB-D cameras) or ex-vivo and post-op imagingacross CT, MR, etc. Registered post-op and ex-vivo datasets to modelsets records 154 and plans records 156 used during intervention canprovide an assessment of execution via volume and mesh intersections.

Certain volume operations can assist in quantitative assessments ofperformance with respect to volume representations. Common volumeoperations include morphology operations such as dilation and erosion,and Boolean operations such as union and intersect. Dilation volumes canbe stored beforehand in model sets records 154 to define planningvolumes, such as Planning Target Volume (PTV) in radiation therapy,negative margin volumes in surgical oncology, or warning/no-fly zonesaround anatomical structures. However, they can be adjusted during asession 164 based on requirements and constraints.

Dilation and erosion, for example, expand and shrink the volume underoperation. Dilation can be used to expand a volume by a specified amountand be used in defining negative margin boundaries and ablation volumes.

The intersection of two volumes can be defined by voxel pairs using theBoolean AND operation. The union of two volumes can be defined by voxelpairs using the Boolean OR operation.

Mesh operations include splitting a polygonal mesh via a cut plane andcalculating the intersection between a cut plane and a mesh, Booleanoperations can be used to calculate union and intersection betweenmeshes, mesh simplification and reduction, spatial smoothing, and spaceportioning operations to query an intersection with a given trajectory.

A needle 1110 (or needle-like instrument) can be defined by itsdirection aligned with its z-axis. Given a set of trajectories, theneedle guidance 1100 can relay needle poses to an active plannedtrajectory 1122 as defined by an entrance point 1120 and a target 1130.

Metrics for needle procedures include a first distance 1114 between aneedle tip 1112 and the target 1130, an angle 1150 between the needle1110 and the planned trajectory 1122, a second distance 1140 between theneedle tip 1112 and a closest point 1124 along the trajectory 1122, andan intersecting point with the plane (not shown).

For ablative procedures, ablation volume can be calculated at the needletip. Coverage can be calculated with updated needle poses by theintersection of the ablation and lesion volumes.

Referring now to FIG. 12 , shown therein is an example planeintersection 1200 where an intersection of a needle 1210 with anultrasound plane 1230 enables out-of-plane advancement of the needle1210.

Plane intersection 1200 can be useful when a needle procedure isperformed under ultrasound guidance. A calibrated and tracked ultrasoundprobe 1220 can register ultrasound video frames to the probe tip.Tracking may be done via sensor attachments onto the ultrasound probe1220 such as infrared reflective fiducials or small radiofrequencycoils. Thus, needle tracking with the tracked ultrasound probe 1220 canprovide out-of-plane guidance towards an intended line-planeintersection 1240. This can provide a significant advantage whencombined with anatomical representations of surrounding structures fromother modalities as traditionally needle advancement under ultrasoundare constrained to be in-plane.

Referring now to FIG. 13 , shown therein is an example of yaw, pitch,and roll pivots 1300 for an osteotome and planar tools.

Given a set of planned cuts, a guided resection can relay feedback ofthe cutting tool poses to the active planned cut plane, where the cutplane is defined by a point and a normal. The metrics module 188 candetermine the distance between the cutting tool's tip and the plannedcut plane, the pitch, yaw, and roll angles, and whether the cutting toolis inside or outside the negative margin volume.

Pitch angle can be calculated through application of the dot productbetween the planned cut plane's normal and tool's z-axis, yaw angle canbe calculated with the dot product of the plane normal with the tool'sy-axis, and roll angle can be calculated with the dot product of theplane normal with the tool's x-axis. FIG. 13 shows an example of the yaw1322, pitch 1332, and roll 1342 tilts on an osteotome 1310. Theconvention can apply to any planar tool, where the y-z plane defines theblade of the tool, with the z-axis being the pointing direction. Wherethe osteotome 1310 is seen from the front, the axes can be seen in afirst orientation 1324. Where the osteotome 1310 is seen from the side,the axes can be seen in a second orientation 1334. Where the osteotome1310 is seen from the top, the axes can be seen in a third orientation1344.

The planned resection volume can be calculated by splitting theanatomical surface representation with each cutting plane in the set.This can enable comparison of the planned resection volume against theguided resection. The planned resection volume is, for example, theresulting model after splitting the anatomical surface representation insequence across all the planned cutting planes. The guided resectionvolume is, for example, the resulting model after splitting in sequencewith the performed cuts as recorded by the tracked instrument. Theguided resection volume in this context can be in reference to thevirtual model and surgical plan, where it may be compared quantitativelyto the real resected specimen after registration with ex-vivo andpost-operative imaging.

Methods described above can be combined to evaluate execution withrespect to surgical plans and tasks. For example, line metrics can beused to evaluate how well a surgeon maneuvers in accordance with theplanned trajectory, quantifying distance to planned trajectory as wellas overshoot/undershoot of target, and jitter.

For geometric resections, evaluations can be made according to theperformed cut relative to the planned cutting plane, including distanceand angles (e.g., at entrance and deep points), overshoot/undershoot attarget depth, and jitter.

Referring now to FIG. 14 , shown therein is a flow chart of an exampleembodiment of a method of managing critical structure avoidance 1400 inthe AR system 100 of FIG. 1A.

Critical structures in the surgical field may be highlighted to avoiddamage/proximity. These can be based on prior imaging modalities wherevolume and surface representations have been segmented and are part ofthe model sets record 154. An example of contoured critical structuresand 3 mm “no-fly zones” created from volume dilation 3300 is shown inFIG. 33 , where the no-fly zones on the left indicate a criticalstructure to be avoided during a procedure. Tracked instruments thatenter the no-fly zone can trigger visual and audio alerts. The system100 may provide visualization of the critical structure and no-fly zonesin a skull model. Segmentations include carotid (red), pituitary (blue),optic nerve (yellow), and orbit (purple). The surrounding dilationregions are the no-fly zones.

In at least one embodiment, the system 100 utilizes the server 110 andthe database 150 to manage avoidance of critical structures. The server110 obtains data models 152 that contain dilation regions aroundsegmented critical structures. The server 110 identifies when trackedinstruments are within the dilation regions (here, “no-fly zones”). Theclient device 170 alerts the user with audio/visual indicators when thetracked instruments go into the no-fly zones.

Critical structure avoidance may assist in AR intervention in situationswhere display devices look directly over the intervention field.Critical structures and no-fly zones may be hidden sub-surface. Criticalstructure avoidance can relay information such as whether a tool isclipping a tumor or a critical anatomy.

Direct in-field overlay for case-relevant data such as saw/drillsettings for orthopedic cases or blood pressure and other vital data inmicrosurgical cases may be displayed on applications across clientdevices 170.

Combinations of surface/volume representations, plan, and patient imagesmay be presented in a virtual window where the user may interact (e.g.,scroll, rotate, zoom) with it throughout the procedure.

Case-specific notes and annotations planned by the surgeon or teampre-operatively may be pulled up with their spatial context and inreference to the surgical plan (e.g., to flag difficult portions of thesurgery, avoidance/awareness of critical aberrant anatomy).

With spatial registration and tracking, the virtual surgical plan can betranslated to a real-time environment where feedback is provided to thesurgeon while performing the procedure, such as by providing visualizingmetrics using the metrics module 188 from geometric resection,needle/tool placements (e.g., biopsies, targeted drug delivery, ablativeprocedures), and critical structure avoidance.

Additional data may be displayed in the field, such as case notes,annotations, and feeds from vitals. These may be anchored spatially tothe reconstructed environment/tracked reference marker, or relative tothe coordinate of the device.

Method 1400 can be divided into an update stage and a render stage,although method 1400 need not be so divided. At 1410, the application172 selects no-fly zone data received from the server 110 and the plansrecord 156. At 1420, the application 172 receives tracked tool inputdata. At 1430, the application 172 executes an inside-mesh evaluator,using the no-fly zone data and tracked tool input data as data forevaluation if the tracked tool tip is inside the no-fly zone. At 1440,the method 1400 branches, depending on the result of 1430. If theinside-mesh evaluator determines that the tool is inside the no-flyzone, the method proceeds to 1450. If the inside-mesh evaluatordetermines that the tool is not inside the no-fly zone, the methodproceeds to 1460. At 1450, the application 172 renders visual and/oraudio alerts. At 1460, the application 172 hides or stops visual and/oraudio alerts.

In method 1400, the processor of the client device 170 may use metricsto produce an AR visualization. The metrics are used to produce aheads-up display (HUD) display of an alert (e.g., if the tool trajectoryintersects or the tool tip is within a no-fly zone, a warning message orwindow border flash is rendered). The AR visualization may be displayedon the primary client device 170, the replicate client device 170 a, orboth. The AR visualization may be produced by showing in-field alertsindicating placement or trajectory of the tracked instrumentintersecting with the no-fly zone.

Geometric Resection

For geometric resection of tumors, a plan is obtained from the plansrecords 156. The plan consists of a set of resection planes to beexecuted. A user can select the active cut plane to evaluate metricsagainst where the metrics are provided by the metrics module 188. Thecutting tool can be quantitatively compared to the planned cut withrespect to angle offsets (e.g., pitch and roll may be the mostimportant) and distance between tip and plane. FIG. 31 illustrates aplayback of navigated osteotomy (or “guided osteotomy”) on the femur3100. The visualizations include a set of planned cuts (blue outline),an active planned cut (green outline), a 5 mm negative margin (orangeoutline), and a tumor (red). The tumor, 5 mm negative margin, andsurgical plan (set of cut planes) can be visualized on top of a physicalmodel. The osteotome can be updated in real time with its position andorientation compared to the current cut in the plan. Real-time metricsfrom the metrics module 188 include distance and angles to the activeplanned cut.

In at least one embodiment, the system 100 utilizes the server 110 andthe databases 150 to manage geometric resection. The server 110 obtainsa surgical plan from the data model 152 that contains a set of cuts tobe performed. The client device 170 enables visualization of thesurgical plan over a physical model. The application 172 compares atracked instrument to an active cut. The application 172 producesmetrics based on the comparison, such as distance and angle to thecutting plane.

Referring now to FIG. 15 , shown therein is a flow chart of an exampleembodiment of a method of managing geometric resection 1500 in the ARsystem 100 of FIG. 1A. Method 1500 can be divided into an update stageand a render stage, although method 1500 need not be so divided. At1510, the application 172 on a client device 170 selects a cut planedata received from the server 110 from the plans record 156. At 1520,the application 172 receives tracked tool input data. At 1530, theapplication 172 executes a plane evaluator, using the selected cut planedata and the tracked tool input data as data for evaluation. Theevaluation can include distances and angles to the plane. At 1540, theapplication 172 renders feedback indicators, such as distance, pitch,roll, and yaw.

In method 1500, the processor of the client device 170 may use metricsto produce an AR visualization. The processor of the client device 170may determine the pose of the tracked instruments, which updates itsgraphical representation. The intersection of the surface model and theplane of the tracked instrument may be used to select faces of thesurface model that is on the intersecting plane, creating a subsectionof the surface model corresponding to an outline. The AR visualizationmay be displayed on the primary client device 170, the replicate clientdevice 170 a, or both. The AR visualization may be produced bygenerating the trajectory of the tracked instrument, outlining anintersection of one of the plurality of active cut planes and the modelset, and displaying a color-coded angle offset and a tip-to-planedistance to indicate precision. The precision may be tolerance orcloseness to the intervention plan.

Needle Placements (Kyphoplasty, Biopsy, Ablation, Etc.)

For needle procedures, plan data from the plans records 156 may beselected where the plan data includes a set of line trajectories definedby an entrance point and target point. The metrics module 188 may thenbe used to generate metrics that include the distance of the needle orinstrument tip to an active trajectory, the distance to a target, aswell as the angle between needle and trajectory. For ablativeprocedures, the ablation volume can be considered in calculation ofcoverage with respect to segmented lesion volume from the model setsrecord 154. The ablation volume can be dynamically positioned withcenter aligned with the needle tip.

An ablative needle procedure example 3200 is shown in FIG. 32 . Asurface representation of a liver lesion is shown in red. An oblique CTslice is in plane with the lesion—a green outline around the lesiondemonstrates that the needle plane is aligned with the lesion.

In at least one embodiment, the system 100 utilizes the server 110 andthe databases 150 to manage needle placement. The server 110 obtains asurgical plans record 156 that contains a set of needle trajectories(e.g., path, entry+target points). The client device 170 visualizes thetrajectory over a physical model, using additional inputs (e.g.,intersecting DICOM slice, ultrasound). The application 172 compares thetracked needle instrument to an active needle path. The application 172uses the metrics module 188 to generate metrics, such as angle toplanned path, distance to target, and distance to path. For ablationneedles, the application 172 visualizes the ablation volume at the tipof the instrument, then calculates coverage (e.g., overlap betweenablation volume and lesion volume).

Referring now to FIG. 16 , shown therein is a flow chart of an exampleembodiment of a method of guiding a needle 1600 in the AR system 100 ofFIG. 1A. Method 1600 can be divided into an update stage and a renderstage, although method 1600 need not be so divided. At 1610, theapplication 172 on client device 170 selects trajectory data from theplan received from the server 110. At 1620, the application 172 receivestracked tool input data. At 1630, the application 172 selects a target.At 1640, the application 172 executes a trajectory evaluator, using theselected trajectory and the tracked tool input as data for evaluation.The evaluation can include a distance and an angle to a line (or linesegment) taken from the selected trajectory. At 1650, the application172 executes a target evaluator, using the tracked tool input data andthe selected target as data for evaluation. The evaluation can include adistance to the target or an intersection. At 1660, the method 1600branches, depending on the result of 1650. If the target evaluatordetermines that the tool intersects with the target, the method proceedsto 1680. If the target evaluator determines that the tool does notintersect with the target, the method proceeds to 1690. At 1670, theapplication 172 renders feedback indicators, such as distances andangles. At 1680, the application 172 renders a target highlight. At1690, the application 172 hides the target highlight.

In method 1600, the processor of the client device 170 may use metricsto produce an AR visualization. The processor of the client device 170may determine the pose of the tracked instruments, which updates itsgraphical representation. The intersection of the surface model and theplane of the tracked instrument may be used to select faces of thesurface model that is on the intersecting plane, creating a subsectionof the surface model corresponding to an outline. The AR visualizationmay be displayed on the primary client device 170, the replicate clientdevice 170 a, or both. The AR visualization may be produced bygenerating a trajectory of the tracked instrument, generating anintersection of a trajectory of the tracked instrument with the targetpoint, generating a line between a tip of the tracked instrument and aplanned line trajectory, and displaying a color-coded tip-to-trajectorydistance, a tip-to-target distance, and an instrument-to-trajectoryangle to indicate precision.

Passive, Visual Walkthroughs

Referring now to FIG. 17 , shown therein is a flow chart of an exampleembodiment of a method of managing a procedure walkthrough 1700 in theAR system 100 of FIG. 1A.

A surgeon can utilize an AR device (e.g., headset, smartphone) tovisualize a standard (e.g., surgical) procedure before performing it toprepare and gain familiarity. The demonstration may also include 3Dmanipulation and physical models, which are defined as follows:

-   -   3D manipulation: the user can interact with the model sets        record 154, including altering the perspective or view angle, or        adding/removing layers of surface and volume representations to        help with visualization; and    -   Physical models: the demonstration may integrate physical model        fusion whereby there exists AR overlays on physical models, and        the surgeon can physically perform aspects of the procedure        while gaining the extra insight provided by the holographic or        otherwise augmented overlays.

In at least one embodiment, the system 100 utilizes the server 110 andthe database 150 to coordinate passive, visual walkthroughs. Data models152 (e.g., anatomical segmentation, surgical plan) are generated inpost-processing at an institution after image acquisition. The datamodels are sent to one or more connected client devices 170 by theserver 110. A client device camera is used to localize a flat surface onwhich to place virtual content. For physical model fusion, the datamodel 152 (e.g., virtual) is aligned through a marker tracked by thecamera. A user may select through and view different steps of theprocedure—if there is playback data, the instrument representations areupdated during playback along with audio if available.

Surgical Procedure Walkthroughs

Moderated walkthroughs combined with traditional education media can beused to enhance education of early-stage learners. These includevisualization of a data model from a model sets record 154 fused orembedded to physical teaching models or textbook illustrations.Annotations and comments can be made by the individual learner orclassmates, where annotations may be textual notes, audio, or videodemonstrating or summarizing the sequence and steps of the procedure.

Method 1700 can be divided into an update stage and a render stage,although method 1700 need not be so divided. At 1710, the application172 on client device 170 selects model set data from the model setsrecords 154 and receives the model set data from the server 110. At1720, the application 172 selects plan data received from the server 110and the plans record 156. At 1725, the application 172 fetches thecurrent step from the plan data. At 1730, the application 172 receivessimulated or playback input data. At 1740, the application 172 selectsinstrument data received from the server 110 and the instruments records160. At 1750, the application 172 executes a step evaluator, using themodel set data, the current step, and an instrument pose as data forevaluation. The output from the step evaluator may include updated steprepresentations and calculations. At 1760, the application 172 updatesinstrument poses based on the instrument inputs, which may be fed backto the step evaluator. At 1770, the application 172 renders steprepresentations based on the output from the step evaluator. At 1780,the application 172 renders audio based on the simulated or playbackinputs. At 1790, the application 172 renders the instruments, based onthe updated instrument poses.

In at least one embodiment, the client device 170, having a processor,can carry out a method for performing AR-assisted surgical procedurewalkthrough, the method comprising: receiving a virtual surgical plan atthe client device; receiving a virtual model at the client device;embedding the virtual model to a physical object using the processor;receiving tool manipulation data from user input at the client device;modifying a view of the virtual model in relation to the physical objectusing the processor based on the tool manipulation data; determiningmetrics by using the processor to apply spatial registration and trackthe tool used in execution of the virtual surgical plan; and providingfeedback at the client device based on the metrics.

Anatomic Visualization

Reference data models from the model set records 152 may be used inisolation or fused with physical models to present an anatomy in 3D.Users may interact with the data through 3D manipulation or view ARvisualizations of simulated real-world actions across procedures (e.g.,osteotomy, radiofrequency ablation) over the virtual or physical models.FIG. 34 demonstrates a visualization of a virtual skull mapped to aphysical object, with selectable fracture patterns 3400. A user mayinteract with the physical object and the model to display fracturepatterns and highlight different parts of the skull.

In at least one embodiment, the system 100 utilizes the server 110 andthe database 150 to manage anatomic visualization. The server 110 sendsreference data models selected from the data model records 152 to clientdevices 170 to be used for visualization and teaching.

Augmenting Scientific Output with FAIR Principles

When the data models records 152 are stored in a human and machineconsumable way with persistent universally unique identifiers, they maybe cross-referenced by traditional scientific output and serve as a newmedium of communication, providing spatial context and dynamic contentto papers and conference proceedings.

For example, see FIG. 35 , which illustrates a surface representation ofa Cone Beam Computed Tomography (CBCT) model from a previously publishedpaper 3500. The model is anchored to the figure (in the publishedpaper), which acts as a reference plane where known spatial points arematched to image points to calculate the pose via homography.

In at least one embodiment, the system 100 utilizes the server 110 andthe database 150 to augment scientific output with FAIR principles. Theserver 110 obtains figures (e.g., from a scientific paper) to use as ARmarkers for overlay. The client device 170 visualizes virtual models orplayback relevant to the figures, and the application 172 spatiallyanchors the virtual models or playback to the figures.

Referring now to FIG. 18 , shown therein is a flow chart of an exampleembodiment of a method of tracking a figure and enhancing a publication1800 in the AR system 100 of FIG. 1A. Method 1800 can be divided into anupdate stage and a render stage, although method 1800 need not be sodivided. At 1810, the client device 170 receives camera input data. At1820, the application 172 selects model data that is received from theserver 110 and the model sets records 154. At 1830, the client device170 receives playback and user input data. At 1840, the application 172extracts features from the camera input data. At 1850, the application172 matches features to references of known spatial objects based on theextracted features. At 1860, the application 172 estimates the camerapose based on matched references of known spatial objects. At 1870, theapplication 172 updates virtual camera pose data to align the virtualcamera to the real camera of the client device 170. At 1880, theapplication 172 updates model representations based on the updatedvirtual camera pose data, the model set data, and the playback and userinput data. At 1890, the application 172 renders figure actors based onthe updated model representations.

In at least one embodiment, the client device 170, having a processor,can carry out a method for performing AR-assisted scientific outputaugmentation, the method comprising: receiving a surface representationof a Cone Beam Computed Tomography (CBCT) model and a correspondingfigure from a journal article at the client device; anchoring the CBCTmodel to the figure image using the processor; calculating a pose usingthe processor by matching known spatial points of the figure image toimage points of the CBCT model via homography; and displaying the poseon a display.

Skills Translation and Evaluation

The metrics module 188 may be used to score performance in acompetency-based skills program. Data models 152 can be developed andstored to teach specific procedures, with recording of performances forreview and feedback. Spatial overlay and practice under guidance withphysical models can help teach best practices and skills.

Holographic or otherwise augmented visualizations of maneuvers and tasksmay provide spatial guidance for the individual to perform. Plans fromthe plans records 156 for procedures may also be used to scoreperformances under tool tracking, either virtually or in combinationwith a physical model. One example is shadowing a sequence of stepsthrough suturing where hand and instrument positions are visualized. Forneedle or geometric resection procedures, evaluation of execution withrespect to the plan includes time, distance, and angles (average andvariance) to the planned trajectory or cut plane, undershoot/overshootat target or depth, and jitter.

In at least one embodiment, the system 100 utilizes the server 110 andthe database 150 to manage skills translation and evaluation. The server110 provides a client device 170 with a session 164 on which individualsmay practice procedures in a guided way (e.g., plan and executionvisualized/played back). The client device 170 evaluates the individualson how well they perform tasks. The application 172 produces and storesmetrics that are generated by metrics module 188 using data from trackedinstruments and/or tracked hands (i.e., the user's tracked handmovements) for score assessment. The application 172 displays themetrics so that the individuals can compare their own statistics acrossattempts as well as against the population.

Self-Assessment for Performance Enhancement

Tracked tools, maneuvers, and tasks performed by an individual may beevaluated against averages across different skill groups and individualperformance statistics.

Learners can flag or comment across steps in performance to convey theirthought process and reasoning for review later by a mentor or teacher.

Formal Assessments in a Standardized Environment

Common data models 152 may be used for formal assessments in astandardized environment with one or more learners in series orparallel. The teacher or reviewer may define objective measurements forassessment or feedback of the procedure (e.g., in place of standardizedpatients of clinical scenario questions).

Referring now to FIG. 19 , shown therein is a flow chart of an exampleembodiment of a method of managing an assessment and review 1900 in theAR system 100 of FIG. 1A. Method 1900 can be divided into an updatestage and a render stage, although method 1900 need not be so divided.At 1910, the user selects a plan using the client device 170, and theselected plan is sent to the server 110. The server 110 then obtains theselected plan data from the plans record 156 and sends it to the clientdevice 170. At 1915, the application 172 receives user and tool inputdata. The user data can be data about how the user is interacting withthe device 170, and the tool data can be data about how the tool ismoved by the user. At 1920, the application 172 receives a taskselection from the user via the client device 170 identifying what taskthe user will perform. At 1925, the client device 170 and/or server 110stores data frames based on the selected task and inputs from the userand tools that perform the task. For example, the inputs can becollected data for the user executing a first cut with a tool (e.g., asaw) in a plan. At 1930, the method 1900 branches, depending on theresult of 1920. If the selected task is flagged as requiring guidance,the method 1900 proceeds to 1935. If the selected task is not flagged asrequiring guidance, the method 1900 proceeds to 1940. At 1935, theapplication 172 provides real-time evaluators based on the selected taskand the user and tool input data. At 1940, the application 172 hidesfeedback indicators, which may be done so that the user is notdistracted with the feedback indicators such as when the user isperforming a movement with a tool, for example. At 1945, the application172 renders feedback indicators to show the user how they haveperformed; this may be done when the user is finished moving the tool.At this point, method 1900 may end if no review is selected. However, ifreview is selected, method 1900 continues at 1950. At 1950, the clientdevice 170 receives peer data frames (e.g., recorded data from peersexecuting the task) from the server 110. At 1955, the application 172aggregates the stored data frames (e.g., the recorded device data, suchas tracked hands or instruments, from task execution), such as thecurrent attempt and historical attempts. At 1960, the application 172receives the plan data from the plans record 156 from the server viaHTTP request and WebSocket and then selects the plan data used foranalysis. At 1965, the application 172 parses task subsets based on oneor more of the peer data frame data, the stored data frame data, and theplan data. The application 172 may do the parsing using metadata fromthe plan. For example, a biopsy plan may have many trajectories, whereeach trajectory is a task. Also for example, a geometric resection planmay contain many cutting planes, where each cut plane is a task. At1970, the application 172 runs analytics on the parsed task subsets toobtain personal statistics. The application 172 can compute the personalstatistics, for example, from stored data obtained at 1915 and/or 1925.At 1975, the application 172 displays personal and/or group statistics.The application 172 can compute the group statistics from peer data,where each peer runs its own instance and has data stored as at 1915 and1925.

Walkthroughs (e.g., Multidisciplinary Difficult Case Rounds)

In another aspect, multiple users may join a session 164 that has beensetup by the server 100 to collaboratively walk through a simulation ofa difficult case to gain shared insight and share perspective on aproposed procedure/surgery. Annotations and case notes may be stored toassist in planning or in context of the surgical plan and tasks.Collaborative walk-throughs may include physical model fusion and 3Dmanipulation where interaction with a model data from the model setsrecords 154 and changes in visualization are updated and shared acrossusers.

Accordingly, in at least one embodiment, the system 100 utilizes theserver 110, database 150, and client devices 170 to coordinatewalkthroughs. The server 110 receives requests from multiple users viatheir client devices 170 to join a session. The server 110 joins two ormore of the users via their client devices 170 to the session. Theserver 110 assigns one of the client devices 170 as the primary clientdevice 170, and the rest of the client devices 170 are denoted asreplicate client devices 170 a. The primary client device 170 dictatesthe data flow and controls viewing options, similar to a presenter in aremote presentation application. The server 110 causes display changesand options to be broadcast from the primary client device 170 to thereplicate client devices 170 a in the session. The server 110 may routeaudio (e.g., on a separate data stream or in packets along with thevideo) to enable two-way audio. The system 100 may be distributed suchthat physical models require each institution or remote user to have themodel used for virtual/real fusion, which can be fabricated through 3Dprinting at each institution.

In at least one embodiment, the system 100 utilizes the server 110 anddatabase 150 to coordinate a surgical procedure walkthrough foreducational purposes. The server 110 obtains reference (or standardized)data sets and additional annotation media from the database 150.

Telementoring

In another aspect, in at least one embodiment, the system 100 mayprovide telementoring capabilities so a remotely located surgeon may beguided by an expert or team from a hospital from across the world. Theserver 110 may be hosted in the cloud or at the institution accessibleremotely through a virtual private network (VPN), enabling authenticatedusers to join a session 164 locally and remotely.

Examples include oncology procedures where video streams are sharedacross users in a session 164. The remote expert may contour over videowhere the resection margin should be to provide real-time guidance. Thecontour may be presented over video, or ray-casted to select elements ofthe underlying surface representations if virtual models are present.Physical models or reference markers may also be fabricated andregistered with client devices 170 across different locations. Tools maythen be positioned by one user in reference to virtual/physical modelsand displayed virtually to others. The user may then shadow the sequenceand steps spatially as indicated by the expert.

Accordingly, in at least one embodiment, the system 100 utilizes theserver 110, database 150, and client devices 170 to coordinatetelementoring. Each client device 170 connects to the server 110 throughWebSocket. The server 110 broadcasts device data between users at thedifferent client devices 170 over WebSockets (e.g., audio, video,tracked instruments, gestures). The server 110 causes the expert user'sinput from a remote device to be broadcast to a remote user. The server110 enables input from the remote user device to be streamed to theexpert user for feedback. In an example sequence of events, the server110 enables: (a) video to be broadcast from the client device of thenovice user to the client device of the expert user; (b) the expert usercan use their client device and application 172 to trace over the modeland over the video generated by the novice user; and (c) theexpert-modified video can then be sent from the client device of theexpert user to the server 110 which can then broadcast theexpert-modified video back to the client device of the novice user.

Individualized Difficult Case Practice/Pre-Execution

Physical models may be fabricated (e.g., one or a combination of 3Dprinting and molding of rigid or flexible materials) from surface orvolume representations generated from patient images to practicedifficult approaches and dissections.

Physical models may be fused with holographic or otherwise augmentedvisualizations. Virtual models and visualizations may include: (1)surface and volume representations from model data from a model setsrecord 154 and plan data from a plans record 156; (2) interactivity andcomparison of the model set data and the plan data in reference toanatomical norms (e.g., facial proportions, measurements, cephalometricdata, etc.); and (3) reference to a normal data set for age/gendermatched normals.

Accordingly, in at least one embodiment, the system 100 utilizes theserver 110, the database 150, and the client device 170 to coordinateindividualized difficult case practice. The database 150 comprises datamodels 152 that include a reference set for age/gender matched normals.The client device 170 produces a visualization of the individual againstthe reference normal set. To produce the visualization, for example, twomodel sets can be registered (e.g., via facial or anatomical features)and overlaid; spatial deviations can be heat mapped to denote areas thatdiffer and degree of magnitude. A clinician may then use the application172 on their client device 170 to measure over the visualization of theindividual and reference sets to measure and analyze differences andsend data on these differences to the server 110. The server 110 maythen store these differences and/or produce a report thereon.

Referring now to FIG. 20 , shown therein is a flow chart of an exampleembodiment of a method of managing remote collaboration 2000 in the ARsystem 100 of FIG. 1A. Method 2000 provides steps (which may or may notoccur in an order, and some of which may be processed concurrently) thatmay be carried out in whole or in part to manage walkthroughs,telementoring, individualized difficult case practice, or other forms ofremote collaboration. Method 2000 can be divided into an update stageand a render stage, although method 2000 need not be so divided.

At 2010, the client device 170 receives model set data from the modelsets record 154 in the database 150. The session creator can specifywhich model set data is to be received by the client device 170. At2015, the client device 170 receives local user input data, such asaudio data, finger gesture data, and tool data. At 2020, the clientdevice 170 processes remote video input data received from the server110. The server 110 can obtain details on what data to select and whichclient device 170 to send the data to from a WebSocket message (e.g.,which is broadcast through a WebSocket connection). At 2025, theapplication 170 executes a model selection evaluator based on the modelset data and the local user input data. At 2030, the application 170executes a pixel selection evaluator based on the local user input dataand the remote video input data. At 2035, the application 170 rendersselected faces based on the output from the model selection evaluator.At 2040, the application 170 renders traced pixels based on the outputfrom the pixel selection evaluator. An “expert” may be designated toprovide the input data sent to the system 100 or to receive the outputsprovided by the system 100 at 2010 to 2040. A “novice” may be designatedto provide the input data sent to the system 100 or to receive theoutputs provided by the system 100 at 2050 to 2085. At 2045, the server110 manages a socket broadcast. The socket can broadcast local userinput data (e.g., from an expert) and local video input data (e.g., froma novice) to generate remote video input data (e.g., to send to theclient device of the expert) and remote user input data (e.g., to sendto the client device of the novice). At 2050, the client device 170receives local video input data (e.g., video from a camera connectedlocally to the client device 170). At 2055, the client device 170processes remote user input data. The client device 170 may process theremote user input data, for example, by determining the location ofinteraction by a user on a render window, ray-casting the location fromthe render window out to determine if part of a model is hit (and if so,which face or vertices). Here, remote and local users can be at twodifferent geo-locations; they may have their own client devices, whereeach person interacts with a render window and underlying modelindependently. At 2060, the client device 170 receives the model setdata from the model sets record 154. At 2065, the application 172executes a pixel selection evaluator based on the local video input dataand the remote user input data. At 2070, the application 172 executes amodel selection evaluator based on the remote user input data and themodel set data from the model sets record 154 to select vertices orfaces of the models in the model set. When selecting parts of the model,a location on the render window (e.g., where user input comes from amouse or touch) can be projected out to determine a part of model is hitto select the underlying face or vertices. At 2075, the client device170 renders audio instructions based on the remote user input data. Forexample, the instructions include how the user should perform a taskbased on what is happening in the video, how to proceed, what to avoid,etc. At 2080, the application 172 renders selected faces based on theoutput from the pixel selection based on the pixel selection evaluator.At 2085, the application 172 renders traced pixels based on the outputfrom the model selection evaluator.

In at least one embodiment, the client devices 170 and the server 110can carry out a modified version of method 2000 for managing multi-userAR collaboration. The method begins with the replicate client device 170a receiving model sets and an intervention plan from the server 110. Themodel sets and the intervention plan can provide visualization andremote guidance. The server 110 receives local user inputs from thereplicate client device 170 a providing remote instructions. The remoteinstructions may be from expert to novice and include how to bestperform an intervention; the remote instructions may be, for example,audio or spatial annotations. The replicate client device 170 a sendsthe local user inputs through the server 110 to the primary clientdevice 170. The local user inputs can provide visual annotation andguidance remotely (e.g., from an expert to a novice). The replicateclient device 170 a displays remote video input in combination with themodel sets and the intervention plan, the model sets including anunderlying surface model. The remote video input may be obtained from areplicate client device 170 a. The remote video may be spatiallyregistered to the intervention field and data models so the novice canperform the intervention with AR guidance and overlay. For example, theperson performing the intervention may have their display device withcamera over the surgical field-of-view. The remote video input, whenfused with the model sets and the intervention plan, provide an ARperspective of the novice and how they are currently performing theintervention. The underlying surface model may be, for example, anunderlying graphic model of patient anatomy or disease. The replicateclient device 170 a executes a pixel selection evaluator based on thelocal user inputs and the remote video input, thereby generating a firstpixel selection output. The pixel selection evaluator maps a pixellocation in a render window to a 3D location of the underlying surfacemodel. The replicate client device 170 a executes a model selectionevaluator based on the model sets and the first pixel selection outputto map a pixel location in a render window to a 3D location of anunderlying surface model, thereby generating a first model selectionoutput. The replicate client device 170 a renders first selected facesof the underlying surface model based on the first model selectionoutput. The face may be a graphic primitive of a surface mesh, which maybe a set of edges (which can be a set of vertices). Selecting a face maythen also include selecting the corresponding edges and vertices. Aselection of faces may be a subsection of the surface mesh. Thereplicate client device 170 a renders first traced pixels based on thefirst pixel selection output. Traced pixels may be obtained when therender window location is captured by an input interface such as acapacitive touch or mouse. The display location may be used to determinethe corresponding pixel location of the video in the render window. Thefirst trace pixels relate to the render window (or display) of theprimary client device 170.

In at least one embodiment, the client devices 170 and the server 110can carry out a modified version of method 2000 for managing multi-userAR collaboration at the primary client device 170 performing the ARintervention. The method begins with the primary client device 170receiving model sets and an intervention plan from the server 110. Theprimary client device 170 processes remote user inputs. The remote userinputs may be, for example, remote instructions from an expert user onthe replicate client device 170 a. The primary client device 170receives local video input. The local video input may be video of theintervention field in which virtual content is overlaid to provide theAR experience (e.g., video from a camera on the display device or via anendoscope). The local video input may be, for example, AR video inputthat allows for real-virtual fusion; this provides a spatial perspectiveof the primary client device 170, which is used to perform theintervention. The primary client device 170 executes a pixel selectionevaluator based on the remote user inputs and the local video input,thereby generating a second pixel selection output. The primary clientdevice 170 executes a model selection evaluator based on the model setsand the remote user inputs, thereby generating a second model selectionoutput. The primary client device 170 renders audio instructions basedon the remote user inputs. The audio instructions may provide, forexample, vocal instructions to novices/learners. The remote user inputsmay be, for example, spatial annotations or audio to help the local userof the primary client device 170. The primary client device 170 renderssecond selected faces based on the second pixel selection output. Theprimary client device 170 renders second traced pixels based on thesecond model selection output. The renderings by the primary clientdevice 170 may allow, for example, the primary client device 170 to drawthe attention of the user of the primary client device 170 to somethingin particular (e.g., what to do with an instrument or tool).

In at least one embodiment, the client device 170 performs one or moreof the AR methods described herein (such as, but not limited to, methodrelated to planning, intervention, guidance, education for medicalapplications). The client device 170 includes a display for displayingAR images. The client device 170 includes a user interface (which may bea combination of hardware and/or software) for receiving user input. Theclient device 170 includes a memory for storing program instructions forperforming the one or more methods. The client device 170 includes aprocessor that is operatively coupled to the display, the userinterface, and the memory, wherein the processor is configured toexecute the program instructions for performing the one or more methods.

Referring now to FIG. 21 , shown therein is a flow chart of an exampleembodiment of a method of application management 2100 in the AR system100 of FIG. 1A. At 2110, the client device 170 receives a request tostart an application. At 2115, the server 110 receives login credentialsfrom a client device 170. The server 110 checks the credentials andreturns access tokens that are required for any client requests or dataaccess for auditing and authorization. At 2120, the server 110 creates asession or allows the user to join an existing session. The server 110sends back success or failure to the client device 170 in a response. At2125, the server 110 sends the specified data model 152 of the sessionto the client device 170, and the client device 170 loads data for theapplication. At 2130, the application 172 sets up a scene. At 2135, theapplication 172 sets up data devices. Data devices can be set up forcommunication and data streaming (e.g., camera device id, resolution,frame rate, IP address/device id of tracking system, which sensors totrack, etc.) At 2140, the client device 170 updates the application 172,based on input/output data and/or state data. At 2145, the application172 renders an image (e.g., visualization, UI). To render an image, a 3Dmodel may contain graphics primitives and geometries that dictate howlight/color should be displayed (e.g., specular, diffuse, ambientproperties, vertex/face normals). The position/orientation of light, thecamera, or the model may dictate the color and illumination at aparticular instance in time, which may combine to compute a color valueat the location, which can then be rasterized for display. At 2150, themethod 2100 branches, depending on whether the user requests to quit theapplication 172. If the user does not request to quit the application,the method goes back to 2140. If the user requests to quit theapplication, the method goes to 2155. At 2155, the application 172performs a cleanup. At 2160, the application 172 causes the user toleave the session. At 2165, the client device 170 quits the application.

Referring now to FIG. 22 , shown therein is a flow chart of an exampleembodiment of a method of login management 2200 in the AR system 100 ofFIG. 1A. At 2210, the server 110 receives a request to start the loginprocess. At 2215, the method 2200 branches depending on whether a newuser is logging in. If it is a new user, the method 2200 continues at2220. If it is not a new user, the method 2200 continues at 2235. At2220, the server 110 receives registration information. At 2225, themethod 2200 branches depending on whether the new user is authorized. Ifthe new user is not authorized, the method 2200 returns to 2220. If thenew user is authorized, the method 2200 continues to 2230. At 2230, theserver 110 creates a new user profile. At 2235, the server 110 receiveslogin information. At 2240, the method 2200 branches depending onwhether the login is valid. If the login is not valid, the method 2200returns to 2235. If the login is valid, the method 2200 continues to2245. At 2245, the server 110 authenticates credentials for the user. At2250, the server 110 ends the login process.

Referring now to FIG. 23 , shown therein is a flow chart of an exampleembodiment of a method of session creation 2300 in the AR system 100 ofFIG. 1A. At 2310, the client device 170 receives a request to start thesession creation process. At 2315, the server 110 receives a session IDfrom the application 172 (e.g., specified by a user) to create asession. At 2320, the method 2300 branches depending on whether thesession ID is unique. If the session ID is not unique, the method 2300returns to 2315. If the session ID is unique, the method 2300 proceedsto 2325. In at least one embodiment, a specified session ID is onlyallowed to contain alphanumeric characters. At 2325, the server 110receives a data model reference for a data model 152 for the sessionthrough the application 172 (e.g., specified by the user). At 2330, themethod 2300 branches depending on whether access is authorized. Ifaccess is not authorized, the method 2300 returns to 2325. If access isauthorized, the method 2300 proceeds to 2335. Access is authorized ifthe user has access credentials to the specified data model 152. At2335, the server 110 links the data model reference to the session. At2340, the server 110 instantiates the session. At 2345, the method 2300branches depending on whether it is a multi-client session 164. If it isnot a multi-client session 164, the method 2300 goes to 2375. If it is amulti-client session 164, the method 2300 proceeds to 2350. At 2350, theclient device 170 (e.g., as determined by the user) grants sessionaccess to another client device 170 of a specified user (i.e., a newsession user) through application 172. At 2355, the method 2300 branchesdepending on whether the new session user exists. If the new sessionuser does not exist, the method 2300 returns to 2350. If the new sessionuser exists, the method 2300 proceeds to 2360. At 2360, the server 110adds user authorization to the session. At 2365, the server 110 invitesthe user to the session. At 2370, the method 2300 branches depending onwhether to add additional users. If there are additional users to add,the method 2300 returns to 2350. If there are not additional users toadd, the method 2300 proceeds to 2375. At 2375, the client device 170ends the session creation process.

Referring now to FIG. 24 , shown therein is a flow chart of an exampleembodiment of a method for joining a session 2400 in the AR system 100of FIG. 1A. At 2410, the client device 170 receives a request to startthe session joining process. At 2420, the application 172 receives asession ID for a session from user input specified by the user at theclient device 170. At 2430, the method 2400 branches depending onwhether the session ID identifies a valid session. If it is not a validsession, the method 2400 returns to 2420. If it is a valid session, themethod 2400 proceeds to 2440. The session ID is checked with the server110. In at least one embodiment, a session is valid if there are nospecial characters in its identifier (i.e., only alphanumeric) and isopen. At 2440, the method 2400 branches depending on whether anauthorized user is trying to join the session. If it is not anauthorized user, the method 2400 returns to 2420. If it is an authorizeduser, the method 2400 proceeds to 2450. The user on the client device170 is an authorized user if the session creator had added them to thelist of session users (e.g., as described in method 2300). At 2450, theserver 110 allows the user to join the session. At 2460, the clientdevice 170 fetches a data model reference for one of the data modelrecords 152 through the server 110. At 2470, the client device 170 endsthe session joining process.

Referring now to FIG. 25 , shown therein is a flow chart of an exampleembodiment of a method of loading data 2500 in the AR system 100 of FIG.1A. At 2510, the client device 170 receives a request to load dataspecified by the user. At 2520, the application 172 retrieves resourcereferences (e.g., references to data in the database 150 as specified bya session creator, for example, as described in method 2300) to datamodels 152 as specified by the user. At 2530, the method 2500 branchesdepending on whether the data is available locally. If the data is notavailable locally, the method 2500 proceeds to 2540. If the data isavailable locally, the method 2500 goes to 2570. At 2540, theapplication 172 gets model data from the data models record 152 throughthe server 110. At 2550, the application 172 calculates checksums forthe data received to make sure there is no error in data transmission.At 2560, the method 2500 branches depending on whether the data downloadwas successful. If the data download was not successful, the method 2500returns to 2540. If the data download was successful, the method 2500proceeds to 2570. The data download is successful if all requested dataare received and matches their checksum. Data not successfully retrieved(e.g., not matching checksum or failed) are re-requested. At 2570, theapplication 172 instantiates objects corresponding to the model setsdata, the plans data, and/or the instruments data. At 2580, the clientdevice 170 ends the data loading process.

Referring now to FIG. 26 , shown therein is a flow chart of an exampleembodiment of a method of setting up a scene 2600 in the AR system 100of FIG. 1A. At 2610, the client device 170 receives a request to set upa scene which includes lights, camera, and actors for rendering. A scenemay contain cameras, lights, and actors to be rendered by a graphicspipeline. At 2620, the application 172 sets up a rendering pipeline formodel sets data and actors (i.e., for the model sets). At 2630, theapplication 172 sets up a rendering pipeline for plan data and actors(i.e., for the plan data). At 2640, the application 172 sets up arendering pipeline for instruments data and actors (i.e., for theinstruments). At 2650, the application 172 sets up virtual cameras andlights. At 2660, the application 172 adds actors to the scene. In atleast one implementation, actors are not rendered and visible untiladded to the scene. At 2670, the application 172 sets up a UI. Theapplication 172 may set up the UI programmatically or through a WYSIWYGeditor of the framework used (such as Unity). At 2680, the client device170 ends the scene setup process.

Referring now to FIG. 27 , shown therein is a flow chart of an exampleembodiment of a method of setting up data devices 2700 in the AR system100 of FIG. 1A. For example, devices may need to be setup for datastreaming (e.g., camera device id, resolution, frame rate, IPaddress/device id of tracking system, which sensors to track, etc.). At2710, the client device 170 receives a request to start setting updevices. Setting up the device may be through the UI of the application172. A user may initiate setting up devices for communication and datastreaming. At 2720, the application 172 specifies device configurations.The device configurations include, for example, which devices are to beused and configured for use (e.g., camera device id, resolution, framerate, IP address/device id of tracking system, which sensors to track,etc.). At 2730, the client device 170 connects the data devices to datadevice channels, allowing data to be sent to and received from otherconnected client devices 170 in a session. At 2740, the method 2700branches depending on whether the devices 170 are remote. If the devices170 are remote, the method 2700 proceeds to 2750. If the devices are notremote, the method 2700 goes to 2760. At 2750, the application 172fetches remote device settings from the server 110. The remote devicesettings may include identifiers and settings required to receive datafrom a remote source—identifiers to know which remote devices areavailable and will stream data, and settings to receive dataappropriately, such as width, height of video frame, and color space. At2760, the application 172 initializes the devices. The application 172may initiate devices to ready them for communication and receive datastreams. At 2770, the application 172 creates tracks and filter chains1064 using the tracks module 186. At 2780, the client device 170 endsthe device setup process.

Referring now to FIG. 28 , shown therein is a flow chart of an exampleembodiment of a method of application cleanup 2800 in the AR system 100of FIG. 1A. At 2810, the client device 170 receives a request to startapplication cleanup from the server 110. At 2820, the application 172stops the update-render loop so that any acquired data is no longer usedto update objects in the application 172 or used for rendering a scene.Objects used for rendering a scene are no longer re-rendered. At 2830,the application 172 stops and deactivates devices. Data is no longerstreamed from local/remote devices or broadcasted to other clients in asession. At 2840, the application 172 aggregates stored data frames.Recorded data from specified data streaming devices are combined withsettings of associated devices, timestamps, and device and dataidentifiers. For example, data may come from multiple devices, such astracked hand data from a gesture interface or tracked surgicalinstruments from a tracking system. At 2850, the method 2800 branchesdepending on whether a performance assessment is requested (e.g., from aUI prompt to the user through the application 172). If a performanceassessment is requested, the method 2800 proceeds to 2860. If aperformance assessment is not requested, the method 2800 goes to 2870.At 2860, the server 110 or application 172 runs analytics and causesstatistics to be displayed. The analytics may depend on, for example,the application context and individual or group statistics. Theanalytics may include amount of jitter in hand/instruments whenperforming a task, spatial deviation from a reference task (such as pathof instrument or angle/distance to planned cut), and time taken toexecute a task. At 2870, the application 172 appends metadata to dataframes and data models 152. Appending the metadata may be done, forexample, so that content adheres to FAIR principles (findable,accessible, interoperable, reusable). At 2880, the application 172 postsmetadata and data recorded or modified to the server 110, which is usedto update the database 150. At 2890, the client device 170 ends theapplication cleanup.

Referring now to FIG. 29 , shown therein is a flow chart of an exampleembodiment of a method of leaving a session 2900 in the AR system 100 ofFIG. 1A. At 2910, the server 110 receives a request from a client device170 (e.g., input by a user) to leave a session. At 2920, the server 110checks data access credentials. If a user is not authorized to accessdata, then data should not persist on the client device 170. At 2930,the method 2900 branches depending on whether the user 162 isauthorized. If the user 162 is not authorized, the method 2900 proceedsto 2940. If the user 162 is authorized, the method 2900 goes to 2950. At2940, the client device 170 deletes data models 152. At 2950, theapplication 172 causes the user to leave the session on the clientdevice 170. At 2960, the server 110 ends the process of leaving asession.

Referring now to FIG. 30 , shown therein is a flow chart of an exampleembodiment of a method of querying metadata 3000 in the AR system 100 ofFIG. 1A. At 3010, the server 110 receives a metadata query request. At3020, the server 110 parses the metadata query parameters. At 3030, theserver 110 executes a database query to the database 150 and aggregatesthe metadata that is obtained from the database 150. At 3040, the server110 filters the metadata by access credentials. The server preservesresults that a client device 170 (e.g., used by a specific user) isauthorized to access. At 3050, the server 110 returns filtered metadatato a user or a client. At 3060, the server 110 ends the metadata query.

Referring now to FIG. 41 , shown therein is a flow chart of an exampleembodiment of a method 4100 of speech to text conversion in the ARsystem 100 of FIG. 1A. In method 4100, a client input mode is added toAR system 100 to improve usability. Along with the traditional keyboardinput, a speech-to-text feature can be used. This application can beinitialized by the client device 170. The client device 170 may usedevice-specific language processing algorithms, such as natural languageprocessing (NLP), to detect the words in the input audio. A text stringis built from this. Once the text has been created, it is displayed onthe client device 170. The client device 170 may have the option ofediting the text through the use of a traditional keyboard. Method 4100may be added to complement features such as chat and annotationcreation.

At 4110, the client device 170 receives audio input from a local user.At 4120, the client device 170 detects audio phonemes from the audioinput. At 4130, the client device 170 carries out language processing onthe detected audio phonemes to determine words that correspond to theaudio phonemes. For example, the client device 170 may determine wordsby analyzing each audio phoneme using adjacent phonemes to determine thecontext and therefore what word it has the highest probability of being.At 4140, the client device 170 uses the processed audio words to createa string representation of the audio input. The server 110 sends thestring representation (e.g., as raw text or formatted text) to theclient device 170. At 4150, the client device 170 renders the stringrepresentation as text in a manner readable by the local user. Method4100 can be divided into an update stage (e.g., 4110 to 4140) and arender stage (e.g., 4150), although method 4100 need not be so divided.In alternative implementations of method 4100, some or all acts carriedout on the client device 170 may be carried out on the server 110, andvice versa (e.g., to speed up processing or to reduce network traffic).

Referring now to FIG. 42 , shown therein is a flow chart of an exampleembodiment of a method 4200 of text to speech conversion in the ARsystem 100 of FIG. 1A. In method 4200, the audio features of the ARsystem 100 are expanded to include the reading of text from manyfeatures to the client devices 170. This may be accomplished throughdevice specific language processing, such as natural language processing(NLP). The words are converted into sound clips that are then combinedinto an audio file that is then played. This feature facilitatesfeatures including instruction texts, chats, and annotation creation andviewing.

At 4210, the server 110 receives a request to generate audio output fora client device 170. At 4220, the server 110 parses text from the audiooutput to words. At 4230, the server 110 converts the words into soundclips. At 4240, the server 110 combines the sound clips into audio. Theserver 110 sends the audio (e.g., as raw audio signals or a formattedaudio file) to the client device 170. At 4250, the client device 170renders the audio in a manner listenable by a user. Method 4200 can bedivided into an update stage (e.g., 4110 to 4140) and a render stage(e.g., 4150), although method 4200 need not be so divided. Inalternative implementations of method 4200, some or all acts carried outon the server 110 may be carried out on the client device 170, and viceversa (e.g., to speed up processing or to reduce network traffic

Experimental Results for Guided Osteotomy

Saw bone phantoms generated from CT were used in AR navigated osteotomy.The data model consisted of the bone tumor volume contoured from CT(visible extent of disease) and a 5 mm extended planning volume toaccount for subclinical microscopic malignant lesions and uncertainty(registration and navigation accuracy). Planar cuts were planned aroundthe planning volume for resection. Two participants executed two cuts ona bone phantom via AR guidance.

Rigid registration was performed using anatomical landmarks on the sawbone phantoms and their CT scans, with fiducial registration error (FRE)of 0.68 mm and 0.62 mm for the two bones respectively. Quantitativemetrics for evaluation included distance to the planned cut, theinstrument pitch angle to the planned cut, and instrument roll angle tothe planned cut.

The AR guidance visualized the tumor volume over real-time video feedalong with the outline of the cut to be executed. A semi-transparentcutting plane was aligned with the movement of the tracked osteotome,where the current intersection of the blade with the anatomy wascalculated in real time and visualized as an outline. This providedvisual feedback as the user was able to visually identify when the cutwas misaligned (shown in FIG. 43 ) or when they were aligned and wereable to proceed with intervention (shown in FIG. 44 ). Distance, pitch,and roll sliders were also used to provide feedback to the user.

Post osteotomy, the saw bones were imaged using a flat panel Conebeam CTfor registration and analysis. Post and pre CT scans were co-registeredusing anatomical landmarks. A best-plane fit was performed using samplepoints of the executed cuts, and pitch, roll, and distance werecalculated against the planned cut. These results are shown in Table 1.

TABLE 1 AR guided osteotomy results on saw bone phantom Pitch RollDistance Bone Cut (degrees) (degrees) (mm) 1 1 0.36 9.03 3.51 1 2 1.7510.67 0.89 2 1 8.47 1.36 1.53 2 2 5.31 3.06 0.25 Average 3.97 6.03 1.54

Experimental Results for Needle Guidance

3D printed molds designed from CT tongue scans were used to cast silicontongue phantoms for AR guidance using a needle instrument. Virtualtargets and planned paths were defined in the CT scans of the siliconphantoms. Two participants performed needle insertion on the phantomsvia AR guidance.

Rigid registration was performed using added fiducials on the siliconphantoms, resulting in FRE of 1.33 mm and 1.10 mm respectively.Quantitative metrics for evaluation included distance to target,distance to planned path, and angle to planned path.

AR guidance visualized the planned path and the target, trajectory ofthe tool, and visualization of the line between the tool tip and closestpoint on the planned path, which provided feedback to the user tovisually minimize deviation before advancing the needle. FIG. 45illustrates visual guidance where the solid line is the planned path,the sphere is the target, and the dashed line represents the trajectoryof the needle.

The tracked needle path and final positions were recorded for angle anddistance comparisons on the silicon phantom. These results are shown inTable 2.

TABLE 2 AR path guidance results on silicon tongue phantom Angle toDistance to Path Tongue Target (mm) (degrees) 1 1.10 6.36 2 2.10 10.45Average 1.60 8.41

While the applicant's teachings described herein are in conjunction withvarious embodiments for illustrative purposes, it is not intended thatthe applicant's teachings be limited to such embodiments as theembodiments described herein are intended to be examples. On thecontrary, the applicant's teachings described and illustrated hereinencompass various alternatives, modifications, and equivalents, withoutdeparting from the embodiments described herein, the general scope ofwhich is defined in the appended claims.

1. A computer-implemented method of guiding augmented reality (AR)intervention using a primary client device and a server, the primaryclient device having a first processor and a first input device, themethod comprising: receiving, at the primary client device, model sets,an intervention plan having an intervention field, and sessioninformation about a session related to the AR intervention from theserver; receiving, at the primary client device, first real-time inputdata from the first input device; generating, at the first processor,metrics by determining an evaluation of an execution of the interventionplan by comparing the intervention plan to the first real-time inputdata; displaying, on the primary client device, real-time graphics,based at least in part on the metrics, spatially over the interventionfield; receiving, at the primary client device, real-time status data,from the server, about a replicate client device connected to the serverafter the replicate client device joins the session; sending, from theprimary client device, the first real-time input data, through theserver, to the replicate client device within the session; sending, fromthe primary client device, the metrics and the evaluation computed fromthe intervention plan, through the server, to the replicate clientdevice within the session; receiving, at the primary client device,second real-time input data from the server, the second real-time inputdata originating from the replicate client device and relating to theintervention plan; and displaying, at the primary client device,real-time graphics based at least in part on the second real-time inputdata from the replicate client device.
 2. The computer-implementedmethod of claim 1, wherein for remotely observing the guided ARintervention using the replicate client device having a second processorand a second input device, the method further comprises: receiving, atthe replicate client device, the model sets, the intervention plan, andthe session information about the session related to the AR interventionfrom the server; receiving, at the replicate client device, the firstreal-time input data, the metrics, and the evaluation broadcasted fromthe primary client device; and displaying, on the replicate clientdevice, real-time graphics based at least in part on the model sets, theintervention plan, the first real-time input data, the metrics, and theevaluation.
 3. The computer-implemented method of claim 2, wherein forproviding remote mentoring of the guided AR intervention, the methodfurther comprises: receiving, at the replicate client device, the secondreal-time input data from the second input device; and sending, from thereplicate client device, the second real-time input data, through theserver, to one or more additional replicate client devices connected tothe server and the primary client device.
 4. The computer-implementedmethod of claim 2, wherein for managing multi-user AR collaboration, themethod further comprises: receiving, at the server, local user inputsfrom the replicate client device providing remote instructions; sendingthe local user inputs through the server to the primary client device;displaying remote video input on the replicate client device incombination with the model sets and the intervention plan, the modelsets including an underlying surface model; executing, by the replicateclient device, a pixel selection evaluator based at least in part on thelocal user inputs and the remote video input, thereby generating a firstpixel selection output; executing, by the replicate client device, amodel selection evaluator based at least in part on the model sets andthe first pixel selection output to map a pixel location in a renderwindow to a 3D location of the underlying surface model, therebygenerating a first model selection output; rendering, on the replicateclient device, first selected faces of the underlying surface modelbased at least in part on the first model selection output; andrendering, on the replicate client device, first traced pixels based atleast in part on the first pixel selection output.
 5. Thecomputer-implemented method of claim 2, wherein for managing themulti-user AR collaboration at the primary client device performing theAR intervention, the method further comprises: processing remote userinputs on the primary client device; receiving local video input fromthe primary client device; executing, by the primary client device, apixel selection evaluator based at least in part on the remote userinputs and the local video input, thereby generating a second pixelselection output; executing, by the primary client device, a modelselection evaluator based at least in part on the model sets and theremote user inputs, thereby generating a second model selection output;rendering audio instructions based at least in part on the remote userinputs at the primary client device; rendering second selected facesbased at least in part on the second pixel selection output at theprimary client device; and rendering second traced pixels based at leastin part on the second model selection output at the primary clientdevice.
 6. The computer-implemented method of claim 2, wherein tosynchronize devices and tracks of the multi-user AR collaboration, themethod further comprises: storing the first real-time input data in afirst buffer in corresponding first device tracks of the primary clientdevice; generating first clock ticks at the primary client device;processing the first real-time input data in the first buffer through afirst filter chain from the first clock ticks; generating first dataframes from the first filter chain; receiving, at the server, the firstdata frames from the primary client device having a first set ofcorresponding time stamps determined from the first clock ticks; storingthe second real-time input data in a second buffer in correspondingsecond device tracks of the replicate client device; generating secondclock ticks at the replicate client device; processing the secondreal-time input data in the second buffer through a second filter chainfrom the second clock ticks; generating second data frames from thesecond filter chain; receiving, at the server, the second data framesfrom the replicate client device having a second set of correspondingtime stamps determined from the second clock ticks; generating, at theserver, combined data frames based at least in part on the first dataframes and the second data frames along with the first set ofcorresponding time stamps and the second set of corresponding timestamps; and storing the combined data frames in a database.
 7. Thecomputer-implemented method of claim 2, further comprising: retrieving,by the server, the combined data frames from the database; generating,by the server, output clock ticks; extracting, by the server, a primaryclient data frame and a primary client time stamp from the combined dataframes for the primary client device corresponding to a current outputclock tick; extracting, by the server, a replicate client data frame anda replicate client time stamp from the combined data frames for thereplicate client device corresponding to the current output clock tick;combining, by the server, extracted data frames of the primary clientdevice and the replicate client device between server time stampscorresponding to current and previous output clock ticks; andbroadcasting, by the server, the combined data frames along withcorresponding time stamps to the primary client device and the replicateclient device.
 8. The computer-implemented method of claim 2, whereinfor guiding geometric resection by AR visualization, the method furthercomprises: obtaining, by the server, a plurality of resection planesfrom the intervention plan; obtaining, by a client device, a pluralityof active cut planes from a tracked instrument from one of the firstreal-time input data or the second real-time input data; determining, bythe client device, the evaluation by comparing at least one of theplurality of active cut planes to at least one of the plurality ofresection planes; calculating, by the client device, the metrics todetermine at least one of angle offset and tip-to-plane distance;calculating, by the client device, the faces of the surface model thatintersects with the plane of the tracked instrument; and producing, bythe client device, the AR visualization by generating the trajectory ofthe tracked instrument, outlining an intersection of one of theplurality of active cut planes and the model set, and displaying acolor-coded angle offset and a tip-to-plane distance to indicateprecision, wherein the client device is the primary client device or thereplicate client device.
 9. The computer-implemented method of claim 2,wherein for guiding needle placement by AR visualization, the methodfurther comprises: obtaining, by the server, a plurality of linetrajectories from the intervention plan, each of the line trajectoriescomprising an entrance point and a target point; obtaining, by a clientdevice, a plurality of active instrument line placements from a trackedinstrument from one of the first real-time input data or the secondreal-time input data; determining, by the client device, the evaluationby comparing at least one of the plurality of active instrument lineplacements to at least one of the plurality of line trajectories;calculating, by the client device, the metrics to determine at least oneof tip-to-trajectory distance, tip-to-target distance, andinstrument-to-trajectory angle; calculating, by the client device, theclosest point between the tracked instrument tip and the plannedtrajectory; and producing, by the client device, the AR visualization bygenerating a trajectory of the tracked instrument, generating anintersection of a trajectory of the tracked instrument with the targetpoint, generating a line between a tip of the tracked instrument and aplanned line trajectory, and displaying a color-coded tip-to-trajectorydistance, a tip-to-target distance, and an instrument-to-trajectoryangle to indicate precision, wherein the client device is the primaryclient device or the replicate client device.
 10. Thecomputer-implemented method of claim 2 wherein for displaying_criticalstructure avoidance by AR visualization, the method further comprises:obtaining, by the server, a first image of an intervention target and acritical structure image of the intervention target from theintervention plan; obtaining, by a client device, a plurality of toolplacements from one of the first real-time input data or the secondreal-time input data from a tracked instrument; determining, by theclient device, the evaluation by comparing at least one of the pluralityof tool placements to a no-fly zone obtained from an overlay of thecritical structure image on the first image; calculating, by the clientdevice, the metrics to determine an incidence of the at least one of theplurality of tool placements with the no-fly zone; and displaying the ARvisualization on the client device by showing in-field alerts indicatingplacement or trajectory of the tracked instrument intersecting with theno-fly zone, wherein the client device is the primary client device orthe replicate client device.
 11. A system for performing guidingaugmented reality (AR) intervention for planning, intervention,guidance, and/or education for medical applications, wherein the systemcomprises: a server including: a database having: a plurality of datamodels that each have a plurality of model set records, a plurality ofplans records, a plurality of recordings records, and a plurality ofinstruments records; a plurality of user records; and a plurality ofsession records; and at least one processor that is operatively coupledto the database and configured to execute program instructions forimplementing: an HTTP server for providing endpoints for queries anddelivery of content, user authentication, and management of sessions;and a WebSocket server to enable multi-client broadcast of data acrossdevice specific listening channels by setting up WebSocket clients; anda primary client device that is communicatively coupled to the server tointeract with the HTTP server and the WebSocket server, the primaryclient device including a first processor and a first input device, theprimary client device being configured to: receive model sets, anintervention plan having an intervention field, and session informationabout a session related to the AR intervention from the server; receivefirst real-time input data from the first input device; generate metricsby determining an evaluation of an execution of the intervention plan bycomparing the intervention plan to the first real-time input data;display real-time graphics, based at least in part on the metrics,spatially over the intervention field; receive real-time status data,from the server, about a replicate client device connected to the serverafter the replicate client device joins the session; send the firstreal-time input data, through the server, to the replicate client devicewithin the session; send the metrics and the evaluation computed fromthe intervention plan, through the server, to the replicate clientdevice within the session; receive second real-time input data from theserver, the second real-time input data originating from the replicateclient device and relating to the intervention plan; and displayreal-time graphics based at least in part on the second real-time inputdata from the replicate client device.
 12. The system of claim 11,wherein the system further comprises the replicate client device, thereplicate client device having a second processor and a second inputdevice, wherein for remotely observing the guided AR intervention thereplicate client device is configured to: receive the model sets, theintervention plan, and the session information about the session relatedto the AR intervention from the server; receive the first real-timeinput data, the metrics, and the evaluation broadcasted from the primaryclient device; and display real-time graphics based at least in part onthe model sets, the intervention plan, the first real-time input data,the metrics, and the evaluation.
 13. The system of claim 12, wherein forproviding remote mentoring of the guided AR intervention: the replicateclient device is configured to: receive the second real-time input datafrom the second input device; and send the second real-time input data,through the server, to one or more additional replicate client devicesconnected to the server and the primary client device.
 14. The system ofclaim 12, wherein for managing multi-user AR collaboration: the serveris configured to receive local user inputs from the replicate clientdevice providing remote instructions and send the local user inputs tothe primary client device; and the replicate client device is configuredto: display remote video input in combination with the model sets andthe intervention plan, the model sets including an underlying surfacemodel; execute a pixel selection evaluator based at least in part on thelocal user inputs and the remote video input, thereby generating a firstpixel selection output; execute a model selection evaluator based atleast in part on the model sets and the first pixel selection output tomap a pixel location in a render window to a 3D location of theunderlying surface model, thereby generating a first model selectionoutput; render first selected faces of the underlying surface modelbased at least in part on the first model selection output; and renderfirst traced pixels based at least in part on the first pixel selectionoutput.
 15. The system of claim 12, wherein for managing the multi-userAR collaboration at the primary client device performing the ARintervention, the primary client device is configured to: process remoteuser inputs; receive local video input; execute a pixel selectionevaluator based at least in part on the remote user inputs and the localvideo input, thereby generating a second pixel selection output; executea model selection evaluator based at least in part on the model sets andthe remote user inputs, thereby generating a second model selectionoutput; render audio instructions based at least in part on the remoteuser inputs; render second selected faces based at least in part on thesecond pixel selection output; and render second traced pixels based atleast in part on the second model selection output.
 16. The system ofclaim 12, wherein to synchronize devices and tracks of the multi-user ARcollaboration: the primary client device is configured to: store thefirst real-time input data in a first buffer in corresponding firstdevice tracks of the primary client device; generate first clock ticks;process the first real-time input data in the first buffer through afirst filter chain from the first clock ticks; and generate first dataframes from the first filter chain; the replicate client device isconfigured to: store the second real-time input data in a second bufferin corresponding second device tracks of the replicate client device;generate second clock ticks at the replicate client device; process thesecond real-time input data in the second buffer through a second filterchain from the second clock ticks; and generate second data frames fromthe second filter chain; and the server is configured to: receive, fromthe primary client device, the first data frames having a first set ofcorresponding time stamps determined from the first clock ticks; receivethe second data frames from the replicate client device having a secondset of corresponding time stamps determined from the second clock ticks;and generate combined data frames based at least in part on the firstdata frames and the second data frames along with the first set ofcorresponding time stamps and the second set of corresponding timestamps; and store the combined data frames in a database.
 17. The systemof claim 12, wherein the server is further configured to: retrieve thecombined data frames from the database; generate output clock ticks;extract a primary client data frame and a primary client time stamp fromthe combined data frames for the primary client device corresponding toa current output clock tick; extract a replicate client data frame and areplicate client time stamp from the combined data frames for thereplicate client device corresponding to the current output clock tick;combine extracted data frames of the primary client device and thereplicate client device between server time stamps corresponding tocurrent and previous output clock ticks; and broadcast the combined dataframes along with corresponding time stamps to the primary client deviceand the replicate client device.
 18. The system of claim 12, wherein forguiding geometric resection by AR visualization: the server isconfigured to obtain a plurality of resection planes from theintervention plan and send the plurality of resection planes to a clientdevice; and the client device is configured to: obtain a plurality ofactive cut planes from a tracked instrument from one of the firstreal-time input data or the second real-time input data; determine theevaluation by comparing at least one of the plurality of active cutplanes to at least one of the plurality of resection planes; calculatethe metrics to determine at least one of angle offset and tip-to-planedistance; calculate the faces of the surface model that intersects withthe plane of the tracked instrument; and produce the AR visualization bygenerating the trajectory of the tracked instrument, outlining anintersection of one of the plurality of active cut planes and the modelset, and displaying a color-coded angle offset and a tip-to-planedistance to indicate precision, wherein the client device is the primaryclient device or the replicate client device.
 19. The system of claim12, wherein for guiding needle placement by AR visualization: the serveris configured to obtain and send a plurality of line trajectories fromthe intervention plan to a client device, where each of the linetrajectories comprise an entrance point and a target point; and theclient device is configured to: obtain a plurality of active instrumentline placements from a tracked instrument from one of the firstreal-time input data or the second real-time input data; determine theevaluation by comparing at least one of the plurality of activeinstrument line placements to at least one of the plurality of linetrajectories; calculate the metrics to determine at least one oftip-to-trajectory distance, tip-to-target distance, andinstrument-to-trajectory angle; calculate the closest point between thetracked instrument tip and the planned trajectory; and produce the ARvisualization by generating a trajectory of the tracked instrument,generating an intersection of a trajectory of the tracked instrumentwith the target point, generating a line between a tip of the trackedinstrument and a planned line trajectory, and displaying a color-codedtip-to-trajectory distance, a tip-to-target distance, and aninstrument-to-trajectory angle to indicate precision, wherein the clientdevice is the primary client device or the replicate client device. 20.The system of claim 12 wherein for displaying critical structureavoidance by AR visualization: the server is configured to obtain andsend a first image of an intervention target and a critical structureimage of the intervention target from the intervention plan to a clientdevice; and the client device is configured to: obtain a plurality oftool placements from one of the first real-time input data or the secondreal-time input data from a tracked instrument; determine the evaluationby comparing at least one of the plurality of tool placements to ano-fly zone obtained from an overlay of the critical structure image onthe first image; calculate the metrics to determine an incidence of theat least one of the plurality of tool placements with the no-fly zone;and display the AR visualization on the client device by showingin-field alerts indicating placement or trajectory of the trackedinstrument intersecting with the no-fly zone, wherein the client deviceis the primary client device or the replicate client device.